A Feasible MPPT Algorithm for the DC/DC Boost Converter: An Applied Case for Stand-Alone Solar Photovoltaic Systems

A Feasible MPPT Algorithm for the DC/DC Boost
Converter: An Applied Case for Stand-Alone
Solar Photovoltaic Systems
711
Original Scientific Paper
Abstract– Oneofthemostpromisingformsofrenewableenergyissolarenergy.However,efficientexploitationofthisenergyformisa
topic of great interest, especially in obtaining the maximum amount of power from the solar photovoltaic (PV) system under changing
environmentalconditions.Tosolvethisproblem,itisnecessarytoproposeanoptimalalgorithm.Therefore,thispaperpresentsafeasible
maximum power point tracking (MPPT) technique for DC/DC boost converters applied in load-connected stand-alone PV systems
to extract the maximum available power. This proposed method is based on the combination of the modified perturb and observe
(P&O) and fractional open circuit voltage (FOCV) algorithms. The effectiveness of the proposed technique is verified via time-domain
simulation of the load-connected stand-alone PV system using PSIM software. The simulation results show a tracking efficiency with
an average value of 99.85%, 99.87%, and 99.96% for tracking the MPP under varying loads, irradiation, and simultaneously varying
temperature,load,andirradiation,respectively.Inaddition,trackingtimeisalwaysstableat0.02secforchangingweatherconditionsin
the large range. Therefore, the results of the proposed method indicate advantages compared to the conventional method.
Keywords: Maximumpowerpointtracking(MPPT),perturbandobserve(P&O),DC/DCboostconverter,photovoltaicsolar
1. INTRODUCTION
According to the global market outlook, global in-
stalled PV capacity will increase to 940 GW by the end
of 2021, a 22% increase from 772.2 GW in 2020, as
shown in Fig. 1 [1]. Such a rapid increase is due to the
ease of installation of this type of energy. For example,
they can be installed in places without other uses, such
as rooftops, deserts, or remote locations. Enhance,
the development of PV energy has become a suitable
research topic in the last decade. However, its power
generation efficiency depends on the characteristics
of the PV module, which vary with solar radiation level
and atmospheric temperature [2]. To maximize energy
from solar absorption at different radiation levels, the
PV model must be driven at its maximum power point
(MPP). In the past decade, a large number of MPP meth-
ods have been developed to increase the efficiency of
the PV module.
Volume 14, Number 6, 2023
Pham Hong Thanh
Thu Dau Mot University,
Electrical Engineering Program, Institute of Engineering and Technology
Thu Dau Mot City, Binh Duong Province, Vietnam
thanhph@tdmu.edu.vn
Le Van Dai*
Industrial University of Ho Chi Minh City,
Electric Power System Research Group, Faculty of Electrical Engineering Technology
Ho Chi Minh City, Vietnam
levandai@iuh.eud.vn
*Corresponded Author
Fig. 1. Global growth in installed PV capacity
2008-2021
Currently, the PV power generation system can be
divided into two types that are grid-connected and
stand-alone PV systems. This paper focuses on the sec-
ond type, which has been widely installed worldwide
due to its low cost and high convenience in installa-

712 International Journal of Electrical and Computer Engineering Systems
Fig. 2. The load-connected stand-alone PV system
with the assistance of the MPPT algorithm
The MPPT algorithms are developed based on crite-
ria including cost, efficiency, loss of energy, tracking
time, level of oscillation, scientific tracking MPP, and
type of power electric converter [5]. Considering these
accounts, it has two MPPT algorithms. The first is con-
ventional methods, which are simple and low-cost but
lead to poor performance. The second has been devel-
oped using intelligent methods, which have high per-
formance and are complex [6].
Over the last few years, there have been many pro-
posed methods to achieve the MPPT under variable
weather conditions. The most significant are methods
such as perturb and observe (P&O) [7], incremental con-
ductance (InC) [8], fractional open circuit voltage (FOCV)
[9], and fractional short circuit current (FSCC) [10].The use
of these methods is effective; however, it has the problem
of slow convergence and significant oscillations around
theMPP.Inaddition,thePVarrayhavingthePVcharacter-
istic is not linear; thus, it needs to apply the MPPT control
methods based on intelligent methods, including neural
network (NN), fuzzy logic control (FLC), and the meta-
heuristic method. Authors in Ref. [11] have proposed the
MPPT method based on the NN-global sliding mode for
DC/DC buck-boost converter.The ANN-FL was developed
byauthorsinRef.[12].ThecombinationoftheInCandFLC
has been proposed by authors in [13]. These combina-
tion methods have several benefits, including being able
to handle variable inputs, avoiding the requirement for
precise mathematical modeling, and having self-conver-
gence and self-learning capabilities [14].The drawback of
these methods is that the tracking performance and out-
put efficiency are dependent on the engineer’s technical
knowledge. To overcome this problem, Manna, S et al.
[15-18] introduced new methods based on the model ref-
erence adaptive control (MRAC) to enhance the tracking
efficiency and speed of PV system under changes in am-
bient conditions. With these algorithms, it gives a reliable
tracking efficiency and time compared to the traditional
P&O, INC, FLC and ANF.
From an algorithmic point of view, even though the
P&O algorithm has many benefits, a rapid change in at-
mospheric circumstances leads this P&O algorithm to
drift away from MPP [19], and authors in Ref. [20] have
providedanalysesofthisdriftissue.Inthisstudy,thedrift
is clearly analyzed in terms of its potential occurrence,
the movement of the operating point, and the effects
of both abrupt changes in insolation and more gradual
changes. As a solution to the drift issue, the authors in
Ref. [21] have applied the constraint on perturbation
step size (ΔD). However, the value of ΔD is high, resulting
in an increase in steady-state power loss [22].The adjust-
able variable step based on the Pythagorean theorem
to calculate the reference voltage through the optimal
value of ΔD is proposed by authors [23, 24]. However, it
is manually adjusted to regulate this ΔD.
According to the literature survey, most are not inter-
ested in the self-adjusting optimal value ΔD under si-
multaneously varying temperature, load, and irradiation
conditions. To solve this problem, this study proposes a
solution that is based on the combination of the modi-
fied P&O and the FOCV algorithms. In this proposed
algorithm, automatic tuning of the step size results in
quick and precise tracking. Large perturbation values
are better for improving dynamic performance, whereas
lower values are better for improving steady-state per-
formance. In addition, the proposed method also con-
siders the drift problem early by setting upper and lower
threshold limits for changes in power based on the slow
and fast changes in the input of solar irradiance. The key
contributions of this work are summarized as follows:
tion and use, especially for use in hard-to-connect or
unconnected areas of the power grid. The stand-alone
PV system refers to generating the electric energy that
supplies the electrical load of the DC and AC types.
This paper focuses on stand-alone PV, which is used to
supply electric energy to a DC load. The architecture of
the isolated-DC grid-connected PV system is proposed
in this study, as shown in Fig. 2. This system can be di-
vided into its key parts, which are the PV array, DC/DC
boost converter, control unit, and load.
In this paper, the stand-alone photovoltaic solar system
is considered to apply for any of the following: heating,
cooking, and water pumping. For example, the authors in
Ref. [3] have used this system to drive the pummel system
using a brushless direct current motor. The battery is not
used in this investigated system to save money and pro-
tect the environment. However, it has a significant prob-
lem in that the amount of the generated electric power
depends on the weather conditions, especially solar ir-
radiance. The efficiency of converting solar energy into
electrical energy from PV panels is very low, usually in the
range of 12% to 30%, due to the variations in irradiation,
temperature, and load [4]. To enhance the conversion
efficiency, the PV array should be tracked at the MPP. To
achieve this goal, the MPP tracking (MPPT) algorithm for
DC/DC converters is required. Basically, the MPPT algo-
rithm is a power control method that adjusts the duty cy-
cleoftheDC/DCconverterbasedontheoutputandinput
of the PV array to capture maximum power production
continuously, thus achieving maximum power and sup-
plyingvoltagestabilityundervaryingweatherconditions.

713
(i) The modeling of the load-connected stand-alone
PV system designed using the PSIM environment and
C++ code to assess the functionality of a PV module.
This system, which consists of a PV array, a DC/DC boost
converter, and an MPPT controller, can be used for any
of the following: heating, cooking, and water pumping;
(ii) Establish a MPPT method based on the combi-
nation of the modified P&O and the FOCV algorithms
to overcome the main drawbacks of the conventional
P&O-MPPT.
(iii) The stability of the proposed method is con-
firmed under simultaneously changing radiation, tem-
perature, and load.
Except for the introduction, this paper consists of four
sections and is organized as follows: Section 2 covers the
modeling,structure,andDC/DCboostconverter.Theprin-
ciple of operation and the schematic diagrams of the DC/
DCboostconverterarepresentedinthissection.Section3
presentsthecontrolschemefortheDC/DCboostconvert-
er of the load-connected stand-alone PV system, followed
by a recall of the conventional P&O-MPPT algorithm and
a proposal for the modified P&O-MPPT algorithm. The ef-
fectiveness of the control method for the DC/DC boost
converter based on the conventional and modified MPPT
algorithms applied in stand-alone photovoltaic solar sys-
tems through other studied cases is verified, analyzed,
discussed, and compared in Section 4. Finally, Section 5
contains the conclusions of this study, and the proposed
directions for future research are presented in this section.
2. PROBLEM FORMULATION
2.1. Photovoltaic array
The proposed single diode mode of the PV module in
thisstudyisshowninFig.3andcanbemodeledbytherela-
tionbetweentheoutputcurrentand voltage asfollows [2]:
( )
0 1
s
q
U R I
kT s
pv
p
U R I
I I I e
R
ς
 
−
 
 
   
+
 
= − − − 
 
 
   
 
(1)
in which U is the output voltage, I is the output current,
q is the electronic charge, ζ is the diode ideality factor, k
is the Boltzmann constant, T is the operating tempera-
ture, Rs
and Rp
are respectively the series and parallel
intrinsic resistances, Ipv
is the photocurrent current and
can determine by Eq. (2), and I0
is the saturation current
and can determine by Eq. (3)
( )
( )
1000
pv sc sc r
I I k T T
λ
= + − −
1 1
3
( )
0
bg
r
E
q
k T T
rs
r
T
I I e
T
ς
−
 
=  
 
(2)
(3)
where λ is the illumination, Ebg
is the band gap for silicon,
ksc
is the short circuit factor, Tr
is the reference tempera-
ture of the standard test conditions, and Irs
is the reverse
saturation, which is given by the following equation.
1
OC
sc
rs U
q
kT
I
I
e ς
 
 
 
=
−
(4)
where Isc
and Uoc
are the short-circuit current and open-
circuit voltage, respectively, which are respectively giv-
en as follows:
. ( )
sc sc r i r
r
G
I I k T T
G
= + − (5)
. r
( ) ln
oc oc r u
r
kT G
U U k T T
q G
ς
= + − + (6)
where G and Gr
are the actual solar radiation and the
reference irradiance at the standard test conditions, re-
spectively; Isc.r
and Uoc.r
are the reference short circuit
current and the reference open circuit voltage at the
standard test conditions, respectively; and ki
and ku
are
the temperature coefficient of Isc
and the temperature
coefficient of Uoc
, respectively.
For practical PV cells, the value of Rp
is large leading to
great influence when the PV operates in the current
sourceregion.Therefore,Eq.(1)canbereducedasfollows:
1
OC
sc
rs U
q
kT
I
I
e ς
 
 
 
=
−
(7)
In the case of a PV array having np
parallel and ns
se-
ries of the PV cells connected together, the current can
be described as follows:
(8)
Fig. 3. The equivalent circuit of the PV module
2.2. DC/DC boost converter model
2.2.1. Circuit description
The DC/DC converter used for the PV system includes
the buck, boost, buck-boost, and single-ended prima-
ry-inductor converters. Based on their advantages, dis-
advantages, and applications [25, 26], the DC/DC boost
converter is discussed and developed in many sectors,
such as industrial drives, adaptive control, battery pow-
er applications, etc., compared to the other ones. Par-
ticularly in the case of the PV application, it not only the
output voltage to the desired level but also performs
the MPPT control. Therefore, DC/DC boost converter is
chosen to study in this paper.
The MPPT-controlled PWM technique for the pro-
posed stand-alone PV systems using the DC/DC boost

converter that is connected between the PV array and
the load as shown in Fig.2. The equivalent circuit dia-
gram is detailed in Fig. 4. The components are used,
including the inductor L, power diode Dw
, MOSFET SW,
and capacitor C as shown in Fig. 4 (a).
Based on the time duration of On or Off for the SW,
the DC/DC boost converter has two distinct modes of
operation, including the continuous conduction op-
eration (CCO) and the discontinuous conduction op-
eration (DCO). For the CCO model, the current through
L is always greater than zero, which means that the L
partially discharges before the switching cycle begins.
For the DCO model, the current through L goes to zero,
which means that the L is fully discharged at the end of
the switching cycle. Because the dynamic order of the
converter is reduced, the DCO model was not selected
compared to the CCO model [27]. Therefore, this study
uses the CCO model for further study.
The process of recharging and discharge will consti-
tute a switching cycle, standing for the obtained out-
put voltage is controlled by the time duration of On or
Off of SW. The PWM technique is applied to adjust the
On or Off duration. The switching period of SW is Tw
,
the SW is closed with time Dw
Tw
and open with (1-Dw
)
Tw
, in which Dw
is the switching duty cycle. The perfor-
mance of the boost converter depends on the input
inductor and the connected load. The boost converter
only operates in the case of RL
≤ RMPP
. Fig. 5 shows the
tracking region of the boost converter on the U-I curve
of the PV [28]. In order to attain the maximum power of
the PV, the Dw
must be changed so that the impedance
values between the load and source are matched. So,
the value of Dw
is determined as follows [28, 29]:
1 MPP
w
L
R
D
R
= − (9)
where RMPP
is the internal resistance of the PV array and
RL
is load resistance.
(a)
(b)
(c)
Fig. 4. The DC/DC boost converter: (a) The equivalent
circuit representation, (b) Equivalent circuit in the
case of turned-on switch SW, (c) Equivalent circuit in
the case of turned-off switch SW
Fig. 5. Tracking region of the boost converter on
the U-I and P-U curves of the PV
2.2.2. Operation analysis
The operation of this boost converter topology de-
pends on the On or Off state of the switch SW and di-
vides into two models.
Model # 1: It begins when the switch SW is turned on
at time zero; the equivalent circuit is shown in Fig. 4 (b).
During this model, the inductor L is connected to the
ground, and the output voltage value is Uo
= Ui
. During
this state, the inductor charged the energy. The current
through the inductor L is raised and calculated by using
Eq. (10).The load RL
is supplied the energy by the capaci-
tor C. In this case, the diode current is equal to zero. The
main operating waveforms of several components, in
this case, are shown in the period (0, Dw
Tw
) of Fig. 6.
w w
0
1
D T
L i
I U dt
L
= ∫ (10)
Model # 2: It begins when the switch SW is turned off
at the time of Dw
Tw
, the equivalent circuit is shown in Fig.
4 (c). During this model, the output voltage in the induc-
tor L is changed and the value is UL
= (Uo
-Ui
). During this
state, the inductor discharged the energy through the
diode to the load. The current through the inductor L is
decayed and calculated by using Eq. (11).The main oper-
ating waveforms of several components, in this case, are
shown in the period (Dw
Tw
, Tw
) of Fig. 6.
( )
w
w w
1
T
L i o
D T
I U U dt
L
=
− −
∫ (11)

715
Fig. 6. Boost converter operating waveforms: (a) the
switch SW; (b) Inductor voltage; (c) Diode current;
(d) Inductor current
(b)
(a)
(d)
(c)
3. MPPT CONTROL METHOD
3.1. DC/DC boost converter
The equivalent circuit of the selected DC/DC boost
converter is shown Fig. 4. The value of Dw
is set up in
the condition between zero to 1 and is considered in
the condition without losses. The output voltage is cal-
culated as follows [30]:
1
1
o i
w
U U
D
=
−
(12)
The inductance value is determined by Eq. (13), and
this value never falls to zero [31]:
2
(1 )
2
w w L
o w
D D R
L
I f
−
=
∆
(13)
inwhich,ΔIo
istheoutputcurrentripplethatisselectedas
1% of the output current, and the switching frequency of
fw
selected is the value of 20 kHz.The capacitance value is
calculated as follows [32]:
o w
o w L
U D
C
U f R
=
∆ (14)
where ΔUo
is the output voltage ripple that is selected
as 1%.
2
( 1)
MPP
L
w
R
R
D
=
− (15)
where Dw
is the switching duty cycle and can be de-
termined as follows:
3.2. MPPT algorithms
There are numerous MPPT algorithms for the DC/DC
converter system based on solar energy systems that
have been put out by numerous researchers with the
shared objective of maximizing power output and op-
erating the system at its maximum power point. The
P&O is a widely popular technique for obtaining the
most power from solar PV due to its ease of use and low
cost in comparison to other MPPT techniques. There-
fore, this paper considers this method to be improved
and uses it as a new method.
3.2.1. Conventional P&O algorithm
The P&O method operates based on observing the
PV power through the sensed values of the voltage and
current of the PV array. Fig. 7 shows the principle of op-
eration of this method, which depends on the calcula-
tion of the output power of the PV array based on the
sensed values of the current and voltage. This power is
compared to the previous one to address the direction
of perturbation and, subsequently, update the switch-
ing duty cycle of the DC/DC converter as follows:
( ) ( 1)
w k w k w
D D D
−
= ± ∆ (17)
where Dw(k)
and Dw(k-1)
are the current and previous per-
turbations of Dw
, respectively; k and (k-1) are the cur-
rent and previous sampling instants.
In general, the PV array power is calculated based on
the sensed values of the voltage and current. The val-
ues of voltage and power at k are stored as P(k)
= U(k)
I(k)
. Then, the power is calculated by using the previous
values at (k - 1). The increment of the voltage and pow-
er of the PV array between two consecutive samples is
determined as follows:
( ) ( 1)
( ) ( 1)
k k
k k
U U U
P P P
−
−
∆ = −



∆ = −


(18)
From Fig. 7, there are three conditions based on the fact
that the slope of the power curve vs. voltage (current) of
the PV array is zero at the MPP and can be described as
follows:
( )
on
w
on off
t
D
t t
=
+ (16)
Fig. 7 . The principle of operation of the
conventional perturb and observe algorithm

By comparing ΔP and ΔU, the algorithm decides
whether to increase or decrease the duty cycle. If the volt-
age increases (positive) and the power increases (posi-
tive) in two consecutive calculation cycles, then the volt-
age will be driven to increase (positive) in the next cycle.
If the voltage increases (positive), that leads to a decrease
in power (negative), and then the voltage is controlled to
decrease (negative) in the next cycle, and vice versa.
From Eq. (12), the output voltage is proportional to
the Dw
, which is determined by Eq. (16) and will be ad-
justed by increasing or decreasing a value called the
“ΔD”, and the updated values between two consecu-
tive samples are determined by Eq. (17). This may be
done repeatedly until the PMPP is achieved [33]. Table
1 lists the overall P&O direction characteristics, and Fig.
8 depicts its flowchart, which can be found in [34-35].
Table 1. The overall P&O direction characteristics
Voltage
perturbation (ΔU)
Change in power
perturbation (ΔP)
Direction of
perturbation (ΔDw
)
+ + +
+ - -
- + -
- - +
3.2.2. Improved P&O algorithm
The conventional P&O algorithm has two main draw-
backs.The first is that the ΔDw
is a fixed value, as shown in
Fig. 7. This affects the process of achieving MPP because
it depends on this ΔDw
jump. If this value is large enough
to reach the MPP quickly, the system will fluctuate widely
around the MPP. Conversely, if the offset is small, the sys-
tem oscillates less around the MPP but takes longer to
arrive at the MPP [36]. The second is that it depends on
the measured voltage and current values, which depend
on the sensors and measurement errors during the sys-
tem's operation. For the measurement error, the system
will measure the values n (usually choose a value from 3
to 7; if this value is too large, it is difficult to respond when
environmental conditions change rapidly) times, then
perform the comparison according to the P&O algorithm
to find the trend in the next operating cycle [37].
To overcome these drawbacks, this study proposes a
solution that the principle of operation of the proposed
method is shown in Fig. 9 and the algorithm flowchart
is shown in Fig. 10. In this proposed algorithm, auto-
matic tuning of the step size results in quick and pre-
cise tracking. Large perturbation values are better for
improving dynamic performance, whereas lower val-
Fig. 8. The flowchart of the conventional algorithm
( ) ( ) ( )
( )
w k k k
D M gradθ
∆ = (19)
where M(k)
is the kth
step size that is altered in ac-
cordance with the PV system's specifications. For this
study, this value is calcaleted as follows:
( )
( )
( )
1
k
k
k
P
M
P −
= (20)
Corresponding to each working point of kth
of PV on
the P-U characteristic curve as shown Fig. 9, the grad
slope is determined as follows:
( )
( )
( )
k
k
k
P
grad abs
U
θ
 
∆
=  
 
∆
 
(21)
where ΔP(k)
and ΔU(k)
are the change in output power
and working voltage of the PV module at the kth
step.
Substituting Eq. (19) into Eq. (17), and it can obtain
as follows:
( ) ( 1) ( ) ( )
( ) ( 1) ( ) ( )
( )
( )
w k w k k k
w k w k k k
D D M grad
D D M grad
θ
θ
−
−
= +



= −


(22a)
(22b)
It is clear from Eq. (22) that the modified automation
complies with the operating point to provide a fast-
tracking capability. As demonstrated in Fig. 9, when the
operational point of the PV system is close to the MPP, the
shift in the PV power and voltage is less significant than
whentheoperationalpointisfarfromtheMPP.Asaresult,
the suggested approach boosts the MPPT tracker’s speed
during abrupt changes in the weather and lowers its os-
cillation during steady-state situations. Additionally, the
suggested approach takes the drift issue into early con-
sideration, Basically, the drift issue occurs when the solar
i) 0
P
U
∆
>
∆
: on the left of MPP, the voltage increases
power increases;
ii) 0
P
U
∆
<
∆
: on the right of MPP, power decreases with
an increase in the voltage;
iii) 0
P
U
∆
=
∆
: at MPP.
ues are better for improving steady-state performance
[37]. The current form of the generic tracking equation
is presented in Eq. (17) above, in which the kth
optimum
value of ΔDw(k)
should be determined as follows:

717
Fig. 9. The principle of operation of the proposed
algorithm
(23)
Considering the irradiance of the PV system operat-
ing under standard test condition GSTC
is 1000 W/m2
,
the new conditions are obtained as follows:
1% is slow change
>1% is fast change
STC
STC
STC
STC
G
G
G
G
∆

<



∆



(24)
The normalized change in solar irradiance is equiva-
lent to the normalized change in power. Therefore Eq.
(22) can be represented as [39]:
1% is slow change
> 1% is fast change
P
P
P
P
∆

<



∆



(25)
where ΔP is the change in power and P represents its
previous iteration. As known, if the irradiance varies and
alters P's value, ΔP's value likewise changes in the same
way.Asaresult,thevalueofΔP/Premainsessentiallycon-
stant under a variety of environmental circumstances. Ad-
ditionally,whentheoperationpointisinthedriftproblem
condition, this value is positive; otherwise, it is negative.
In order to address the drift issue as soon as possible, a
constant value of ΔP/P is inserted at the beginning of the
program, as illustrated in Fig .10. In this paper, the value
of ΔP/P is chosen as 0.01. Under various weather circum-
stances, the MPP voltage is computed at roughly 78% of
the open circuit voltage. In order for the suggested meth-
od to determine the side of the operational point when
the solar irradiance varies quickly, the Uset
is applied as
76% of the open circuit voltage [39]. The operation point
is to the right of the MPP if the PV voltage is greater than
the Uset
, which causes the Dw
reference to decrease. If not,
Fig. 10. The flowchart of the improved perturb and
observe algorithm
irradiation on the PV array rapidly increases by at least 10
Ws/m2
[17]. The input of solar irradiance is thus depen-
dent on the following two requirements for change in the
solar irradiance perturbation ΔGSTC
[38]
the Dw
reference increases, and the ΔP/P shrinks dramati-
callywhentheoperationpointisneartheMPP.Asaresult,
the control unit enters the conventional P&O method to
determine the precise optimum MPP.
4. RESULTS AND DISCUSSION
To assess the efficacy of the recommended method, a
PSIM model for the proposed standalone PV system with
MPPT algorithm has been developed, as shown in Fig.
11. A PV array, a DC/DC boost converter with an MPPT
controller, and a resistive load are all components of the
system under examination.Table 2 summarizes the elec-
trical characteristics of the BP MSX 60 PV panel, which
is used as a standard, under normal test conditions. The
DC/DC boost converter design parameters are shown in
Fig. 11 and were based on Section 3.The resistive load is
adjustable and ranges in value from 50 Ω to 200 Ω.
Fig. 11. Simulation model stand-alone photovoltaic
system with MPPT algorithm
The P-U and I-U characteristics of a simulated BP MSX
60 PV panel for the irradiances of 200 W/m2
and 1000
10 is slow change
ÄG >10 is fast change
STC
STC
G
∆ <


 10 is slow change
ÄG >10 is fast change
STC
STC
G
∆ <




Fig. 12. The I-U and P-U characteristics for a module
on the irradiances of 200 W/m2
and 1000 W/m2
at 25 o
C
Table 2. Electrical characteristics of the PV module
Parameters Values
The maximum power (Pmax) 60 W
The voltage at Pmax (Umpp) 17.1 V
The current at Pmax (Impp) 3.5 A
The open circuit voltage (Uoc) 21.1 V
The short circuit current (Isc) 3.8 A
The temperature coefficient of Uoc -(80 ± 10) % V/0
C
The temperature coefficient of Isc -(0.065 ± 0.015) % V/0
C
The temperature coefficient of power -(0.5 ± 0.05) % V/0
C
The nominal operating cell temperature 47 ± 2 0
C
The operating temperature 25 0
C
When the sun irradiation varies, it is not possible to
manually adjust the load resistance with the variable
value from 50 Ω to 200 Ω. Therefore, the MPPT algo-
rithm and DC/DC boost converter have been designed
in Section 3 to continuously adjust the duty cycle of the
converter. Two scenarios are considered to verify the
perfection of the proposed system.
Case 1: Simulation results for the varying load under
the fixed irradiation: The tested system has been simu-
lated for two predefined load levels of 100 Ω and 150 Ω.
The fixed ambient temperature of 25 o
C and the fixed
irradiation of 1000 W/m2
are considered inputs to the
PV panel. Fig. 13 shows the comparison of the output
power of the PV panel of both MPPT algorithms. Ob-
serving the dynamic response, the performance and
efficiency of the proposed method are better in com-
parison with the conventional P&O algorithm in terms
of response time and output power oscillations. The
proposed method has a fine response and less fluctua-
tion around the MPP than the conventional method. In
the case of RL
= 100 Ω, it takes 0.05 seconds to reach
the MPP point when applying the conventional MPPT
method, whereas using the proposed MPPT method, it
is 0.015 seconds, as shown in Fig. 13 (a).
100 , for 0.2 sec
50 , for 0.2 sec 0.5 sec
( )
150 , for 0.5 sec 0.7 sec
200 , for 0.7 sec
L
t
t
R t
t
t
Ω ≤

 Ω < ≤

= 
Ω < ≤

 Ω >

(26)
The dynamic response is shown in Fig. 14, and the
simulated results are summarized in Table 3. From this
table, it can be seen that the proposed MPPT method
presents better results than the conventional MPPT
method in terms of response time, efficiency, and oscil-
lations to reach the MPP point. The efficiency for track-
ing MPP is expressed by using Eq. (27) below, in which
the maximum power is 60W. As a result, the average ef-
ficiency and tracking time, in this case, are 99.85% and
0.0375 sec, respectively.
max
100%
o
P
P
η = (27)
In addition, a simulation for the predefined varying
load is tested to verify the output response for the PV
according to the following structure.
(a)
(b)
Fig. 13. The output power of a PV panel at 25 o
C
with the input irradiance of 1000 W/m2
: (a) the load
of 100 Ω, (b) the load of 150 Ω
W/m2
at 25 o
C are shown in Fig.12. As a result, the MPP
powers have changed from 10.66 W to 59.6 W, and the
MPP voltages have changed from 16.88 V to 17.14 V
corresponding to the insolation level of 200 W/m2
and
1000 W/m2
, respectively.
Fig. 14. The output power of a PV panel at 25o
C
with the input irradiance of 1000 W/m2
under
considering the predefined varying load

719
Table 3. A comparison of the properties of both methods for the predefined varying load and the fixed irradiation
Time (s) RL
(Ω) Pmax
(W)
Conventional algorithm Proposed algorithm
Po
(W) η (%) Tracking time (s) Po
(W) η (%) Tracking time (s)
0-0.2 50 60 49.01 81.68 0.11 59.89 99.82 0.03
0.2-0.5 100 60 58.80 98.00 0.11 59.92 99.87 0.03
0.5-0.7 150 60 58.20 97.00 0.11 59.91 99.85 0.04
0.7-1 200 60 57.64 96.07 0.09 59.92 99.87 0.05
Average - - - 93.19 0.105 - 99.85 0.0375
Case 2: Simulation results for the varying irradiation
under the fixed load: In this scenario, the input irradia-
tion varies in a range of 200 W/m2
to 1000 W/m2
at the
time from 0 to 1 second, the temperature operation is
kept at 25o
C, and the fixed load is 50 Ω according to the
following structure.
2
2
2
2
2
200W/m , for 0.2 sec
1000W/m , for 0.2 sec 0.4 sec
irradiance( ) 300W/m , for 0.4 sec 0.6 sec
(300-1000)W/m , for 0.6 sec 0.8 sec
1000W/m , for
t
t
t t
t
≤
< ≤
= < ≤
< ≤
0.8 sec
t







 >

(28)
The comparative output power of the two MPPT algo-
rithms is shown in Fig. 15. According to the findings, the
suggested method's power tracker addresses the input
irradiance's correct direction, whereas the traditional
method's tracking power does not when the input ir-
radiation abruptly changes. Notably, the suggested ap-
proach's converter duty cycle caused the drift issue to
affect the traditional method more than it did. In Fig. 16,
this converter duty cycle is shown.Table 4 lists the simu-
lated outcomes for both techniques. The efficiency for
tracking MPP is expressed by using Eq. (27), in which the
maximum power is defined by predefined power levels.
Fig. 15. The output power of a PV panel at 25o
C
with considering the varying irradiance
According to the results in Table 4, the proposed
method's MPPT efficiency under all the different weath-
er condition scenarios achieves an average tracking ef-
ficiency of 98.87% for the drift problem under sudden
changes in weather conditions (suddenly increasing,
suddenly decreasing, or linearly decreasing the input
solar irradiation). The suggested strategy lowers the
oscillation around the MPP under steady-state circum-
stances and swiftly follows the MPP during changes in
weather, according to the findings of the simulations.
In addition, compared to the typical approach, the out-
put PV power is greater.
Fig. 16. The converter duty of a PV panel at 25o
C
with considering the varying irradiance
Case 3: Simulation results under simultaneously
varying temperature, load, and irradiation: this scenar-
io is carried out to verify the effectiveness of the pro-
posed method for the PV system under different value
range of the temperature, load, and irradiation for a
period of 3 sec as shown in Fig. 17. The response of the
output power of the PV system for each time when ap-
plying two method is shown in Fig. 18. The simulation
results are summarized in Table 5.
Observing the obtained results shows that under
different random changing conditions that affect the
Fig. 17. The simultaneously varying temperature,
load, and irradiation
Fig. 18. The conveter duty of a PV panel under
simultaneously varying temperature, load, and
irradiation

720
survey system, the proposed method still achieves high
tracking efficiency, with the lowest value of 99.71% and
the highest up to 99.97%; the average efficiency in this
case is 99.85%.The tracking time is always stable at 0.02
sec for condition of the large range of the temperature,
load, and irradiation.
Contrary, the time is almost instantaneous (less
than 0.005 sec). As a result, the proposed method
gets better efficiency than the method of Ref [40]; the
average efficiency under varying temperatures is 98.33
%, 96.475%, and 99.825% when applying FL, ANN, and
ANN-fuzzy, respectively.
Table 4. A comparison of the properties of both methods for the fixed load and suddenly changed irradiation
Time (s) Irradiation (W/m2
) Pmax
(W)
Po
0 - 0.2 200 11.4 11.35 99.56 0.09 11.38 99.82 0.04
0.2 - 0.4 1000 60 50.00 83.33 0.30 59.93 99.88 0.015
0.4 - 0.6 300 17.49 17.32 99.03 0.11 17.48 99.94 0.07
0.6 - 0.8 300 - 1000 39.02 34.18 87.60 N/A 38.99 99.87 N/A
0.8 - 1.0 1000 60 50.00 83.33 N/A 59.91 99.85 N/A
Average - - - 90.57 - - 99.87 -
Table 5. A comparison of the properties of both methods
under simultaneously varying temperature, load, and irradiation
Time (s) Pmax
(W)
Po
0-0.5 11.40 11.38 99.82 0.04 11.38 99.82 0.02
0.5-1.0 57.85 49.49 85.55 0.07 57.68 99.71 0.02
1.0-1.5 17.2 17.09 99.35 0.05 17.19 99.94 0.0015
1.5-2.0 37.53 32.87 87.59 N/A 37.49 99.89 N/A
2.0-2.3 55.16 47.21 85.58 0.02 55.05 99.80 0.005
2.3-2.5 32.96 29.78 90.35 0.002 32.93 99.91 0.002
2.5-3.0 32.95 29.11 88.35 0.04 32.94 99.97 0.02
Average - - 90.94 - - 99.86 -
5. CONCLUSION
The marketability of photovoltaic solar energy will
be heavily influenced by its efficiency, stability, and
dependability. This paper has developed the control
method for the DC/DC boost converter based on the
MPPT algorithm applied in a stand-alone photovoltaic
solar system in order to respond well and complete oth-
er renewable energy sources from a technical aspect.
The main purpose is to improve the tracking efficiency,
tracking speed, and oscillation related to changing the
temperature, load, and irradiation, which are the main
drawbacks of the conventional P&O-MPPT.
The PV panel type of the BP MSX 60 PV is considered
when developing the mathematical model. The DC/
DC boost converter is designed in accordance with the
MPPT algorithm with the objective of maximizing power
output and operating the system at its maximum power
point. The design of the DC/DC boost converter and the
modification of the conventional P&O-MPPT algorithm
have been explained in deep detail. For the design of the
DC/DC boost converter, the CCO model is applied. The
constraint on perturbation step size is selected based on
the impedance value between the load and source and
is considered in the condition without losses.The induc-
tance and capacitance values are calculated based on
the output current, a voltage ripple of 1%, and a switch-
ing frequency of fw
set to 20 kHz. For the MPPT algo-
rithm, it uses the modified perturb and observe (P&O)
and fractional open circuit voltage (FOCV) algorithms
to determine the duty ratio with an adaptive step size.
In addition, a combination of current changes as well
as power and voltage changes is considered in the de-
cision-making process to avoid the drift problem early.
The simulation validation of the proposed and con-
ventional P&O algorithms was presented and compared
using study cases ofvarying loads under fixed irradiation
and changing irradiation under a fixed load. The simu-
lation results show that the proposed MPPT technique
achieves efficiency with an average value of 99.85%,
99.87%, and 99.96% for tracking the MPP under varying
loads, irradiation, and simultaneously varying tempera-
ture, load, and irradiation, respectively. The suggested
strategy lowers the oscillation around the MPP under
steady-state circumstances. It swiftly tracks the MPP dur-
ing weather changes, according to the findings of the
simulations. In addition, the output PV power is more
significant compared to the conventional approach.
6. ACKNOWLEDGMENTS
This research is funded byThu Dau Mot University, Binh
Duong Province, Vietnam under grant number DT.21.1-
068.Theauthorsalsoappreciatethecommentsofreview-
ers and the support of Electric Power System Research
Group, Industrial University of Ho Chi Minh City,Vietnam.
International Journal of Electrical and Computer Engineering Systems

721
7. REFERENCES:
[1] S. Michael, “Global Market Outlook for Solar Power
2020-2026”, https://www.solarpowereurope.org/in-
sights/market-outlooks/global-market-outlook-for-
solar-power-2022#downloadForm (accessed: 2022)
[2] M. G. Villalva, J. R. Gazoli, E. Ruppert Filho, “Com-
prehensive approach to modeling and simulation
of photovoltaic arrays”, IEEE Transactions on Power
Electronics, Vol. 24, No. 5, 2009, pp. 1198-1208.
[3] B. Abdellahi, M. E. M. Mohamed Mahmoud, N. O. Dah,
A. Diakité, A. El Hassen, C. Ehssein, “Monitoring the
performances of a maximum power point tracking
photovoltaic (MPPT PV) pumping system driven by a
brushless direct current (BLDC) motor”, International
Journal of Renewable Energy Development, Vol. 8,
No. 2, 2019, pp. 193-201.
[4] D. Sera, R. Teodorescu, P. Rodriguez,“PV panel model
based on datasheet values”, Proceedings of the IEEE
International Symposium on Industrial Electronics,
Vigo, Spain, 2007, pp. 2392-2396.
[5] N. Karami, N. Moubayed, R. Outbib, “General review
and classification of different MPPT Techniques”, Re-
newable and Sustainable Energy Reviews, Vol. 68,
2017, pp. 1-18.
[6] R. Alik, A. Jusoh, “An enhanced P&O checking algo-
rithm MPPT for high tracking efficiency of partially
shaded PV module”, Solar Energy, Vol. 163, 2018, pp.
570-580.
[7] K. H. Hussein, I. Muta, T., Hoshino, M. Osakada,“Maxi-
mum photovoltaic power tracking: an algorithm for
rapidly changing atmospheric conditions”, IEE Pro-
ceedings-Generation,Transmission and Distribution,
Vol. 142, No. 1, 1995, pp. 59-64.
[8] B. Liu, S. Duan, F. Liu, P. Xu, “Analysis and improve-
ment of maximum power point tracking algorithm
based on incremental conductance method for pho-
tovoltaic array”, Proceedings of the IEEE 7th
Interna-
tional Conference on Power Electronics and Drive
Systems, Bangkok, Thailand, 27-30 November 2007,
pp. 637-641.
[9] J. Ahmad, “A fractional open circuit voltage based
maximum power point tracker for photovoltaic ar-
rays”, Proceedings of the IEEE 2nd
International Con-
ference on Software Technology and Engineering,
San Juan, PR, USA, 3-5 October 2010, V1-247-V1-250.
[10] M. A. Masoum, H. Dehbonei, E. F. Fuchs, “Theoretical
and experimental analyses of photovoltaic systems
with voltage and current-based maximum power-
point tracking”, IEEE Transactions on Energy Conver-
sion, Vol. 17, No. 4, 2002 pp. 514-522.
[11] I. U. Haq, Q. Khan, S. Ullah, S. A. Khan, R. Akmeliawati,
M. A. Khan, J. Iqbal, “Neural network-based adap-
tive global sliding mode MPPT controller design for
stand-alone photovoltaic systems”, Plos One, Vol. 17,
No. 1,2022 e0260480.
[12] E. Karatepe, T. Hiyama, “Artificial neural network-
polar coordinated fuzzy controller based maximum
power point tracking control under partially shaded
condition”, IET Renewable Power Generation, Vol. 3
No. 2, 2009, pp. 239-253.
[13] Y. N. Anagreh, A. Alnassan, A. Radaideh, “High Per-
formance MPPT Approach for Off-Line PV System
Equipped with Storage Batteries and Electrolyzer”,
International Journal of Renewable Energy Develop-
ment, Vol. 10, No. 3, 2021, 507-515.
[14] S. Ullah, Q. Khan, A. Mehmood, S. A. M. Kirmani, O.
Mechali, “Neuro-adaptive fast integral terminal slid-
ing mode control design with variable gain robust
exact differentiator for under-actuated quadcopter
UAV”, ISA Transactions, Vol. 120, 2022, pp. 293-304.
[15] S. Manna, D.K. Singh, A.K. Akella, H. Kotb, K.M. AboRas,
H.M. Zawbaa, S. Kamel, “Design and implementation
of a new adaptive MPPT controller for solar PV sys-
tems”, Energy Reports, Vol. 9, 2023, pp.1818-1829.
[16] S. Manna, A. K. Akella, D. K. Singh,“A novel MRAC-
MPPT scheme to enhance speed and accuracy in PV
systems“, Iranian Journal of Science and Technology,
Transactions of Electrical Engineering, Vol. 47, No. 1,
2023, pp.233-254.
[17] S. Manna, D. K. Singh, A. K. Akella, A. Y. Abdelaziz, M.
Prasad, "A novel robust model reference adaptive
MPPT controller for Photovoltaic systems", Scientia
Iranica, 2022.
[18] S. Manna, A. K. Akella, D. K. Si, D. K. Singh, "Imple-
mentation of a novel robust model reference adap-
tive controller-based MPPT for stand-alone and grid-
connected photovoltaic system", Energy Sources,
Part A: Recovery, Utilization, and Environmental Ef-
fects, Vol. 45, No. 1, 2023, pp. 1321-1345.
[19] D. P. Hohm, M. E. Ropp, “Comparative study of maxi-
mum power point tracking algorithms”, Progress in
photovoltaics: Research and Applications, Vol. 11,
No. 1, 2003, pp. 47-62.

722
[20] D.Sera,L.Mathe,T.Kerekes,S.V.Spataru,R.Teodorescu,
“On the perturb-and-observe and incremental con-
ductance MPPT methods for PV systems”, IEEE Journal
of Photovoltaics,Vol. 3, No. 3, 2013, 1070-1078.
[21] N. Femia, G. Petrone, G. Spagnuolo, M. Vitelli, “Opti-
mization of perturb and observe maximum power
point tracking method”, IEEE Transactions on Power
Electronics, Vol. 20, No. 4, 2005, pp. 963-973.
[22] M. Killi, S. Samanta, “Modified perturb and observe
MPPT algorithm for drift avoidance in photovoltaic
systems”, IEEE Transactions on Industrial Electronics,
Vol. 62, No. 9, 2015, pp. 5549-5559.
[23] A. Charaabi, A. Zaidi, O. Barambones, N. Zanzouri,
“Implementation of adjustable variable step based
backstepping control for the PV power plant”, Inter-
national Journal of Electrical Power & Energy Sys-
tems, Vol. 136, p.107682.
[24] Y. Yang, F. P. Zhao, “Adaptive perturb and observe
MPPT technique for grid-connected photovoltaic in-
verters”, Procedia Engineering, Vol 23, 2011, pp. 468-
473.
[25] S. Dahale, A. Das, N. M. Pindoriya, S. Rajendran, “An
overview of DC-DC converter topologies and con-
trols in DC microgrid”, Proceedings of the IEEE 7th
International Conference on Power Systems, Pune,
India, 21-23 December 2017, pp. 410-415.
[26] M. H. Taghvaee, M. A. M. Radzi, S. M. Moosavain, H.
Hizam, M. H. Marhaban, “A current and future study
on non-isolated DC–DC converters for photovoltaic
applications”, Renewable and Sustainable Energy Re-
views, Vol. 17, 2013, pp. 216-227.
[27] M. H. Rashid, “Power electronics handbook”, Butter-
worth-Heinemann, 2017.
[28] O. P. Mahela, A. G. Shaik, “Comprehensive overview
of grid interfaced solar photovoltaic systems”, Re-
newable and Sustainable Energy Reviews, Vol. 68,
2017, pp. 316-332.
[29] P. V. Mahesh, S. Meyyappan, R. Alla, “Support Vec-
tor Regression Machine Learning based Maximum
Power Point Tracking for Solar Photovoltaic systems”,
International Journal of Electrical and Computer En-
gineering Systems, Vol. 14, No. 1, 2023, pp. 100-108.
[30] A. J. Mahdi, S. Fahad, W. Tang, “An adaptive current
limiting controller for a wireless power transmission
system energized by a PV generator”, Electronics,Vol.
9, No. 10, 2020, p. 1648.
[31] S. Fahad, A. J. Mahdi, W. H. Tang, K. Huang, Y. Liu,
“Particle swarm optimization based DC-link voltage
control for two stage grid connected PV inverter”,
Proceedings of the IEEE International Conference on
Power System Technology, Guangzhou, China, 6-8
November 2018, pp. 2233-2241.
[32] S. K. Kollimalla, M. K. Mishra, “A novel adaptive P&O
MPPT algorithm considering sudden changes in the
irradiance”, IEEE Transactions on Energy Conversion,
Vol. 29, No. 3, 2014, pp. 602-610.
[33] K. L. Lian, J. H. Jhang, I. S. Tian, “A maximum power
point tracking method based on perturb-and-ob-
serve combined with particle swarm optimization",
IEEE Journal of Photovoltaics, Vol. 4, No. 2, 2014, pp.
626-633.
[34] T. Esram, P. L. Chapman,“Comparison of photovoltaic
array maximum power point tracking techniques”,
IEEE Transactions on Energy Conversion, Vol. 22, No.
2, 2007, pp. 439-449.
[35] S. Jain, V. Agarwal, “Comparison of the performance
of maximum power point tracking schemes applied
to single-stage grid-connected photovoltaic sys-
tems”, IET Electric Power Applications, Vol. 1, No. 5,
2007, pp. 753-762.
[36] G. C. Mahato, T. R. Choudhury, “Study of MPPT and
FPPT: A brief comparison”, Proceedings of the IEEE
17th
India Council International Conference, New
Delhi, India, 10-13 December 2020, pp. 1-7.
[37] L. Piegari, R. Rizzo, “Adaptive perturb and observe
algorithm for photovoltaic maximum power point
tracking”, IET Renewable Power Generation, Vol. 4,
No. 4, 2010, pp. 317-328.
[38] S. D. Al-Majidi, M. F. Abbod, H. S. Al-Raweshidy, “A
novel maximum power point tracking technique
based on fuzzy logic for photovoltaic systems”, Inter-
national Journal of Hydrogen Energy, Vol. 43, No 31,
2018, pp. 14158-14171.
[39] J. Ahmad, “A Fractional Open Circuit Voltage Based
Maximum Power Point Tracker for Photovoltaic Ar-
rays”, Proceedings of the 2nd International Confer-
ence on Software Technology and Engineering, San
Juan, PR, USA, 3-5 October 2010, pp. 247-250.
[40] L. Hichem, O. Amar, M. Leila, “Optimized ANN-fuzzy
MPPT controller for a stand-alone PV system under
fast-changing atmospheric conditions”, Bulletin of
Electrical Engineering and Informatics, Vol. 12, No. 4,
2023, pp. 1960-1981.
International Journal of Electrical and Computer Engineering Systems

A Feasible MPPT Algorithm for the DC/DC Boost Converter: An Applied Case for Stand-Alone Solar Photovoltaic Systems

More Related Content

Similar to A Feasible MPPT Algorithm for the DC/DC Boost Converter: An Applied Case for Stand-Alone Solar Photovoltaic Systems (20)

Recently uploaded (20)

A Feasible MPPT Algorithm for the DC/DC Boost Converter: An Applied Case for Stand-Alone Solar Photovoltaic Systems