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Fuzzy logic based MPPT controller for high
conversion ratio quadratic boost converter
Saban Ozdemir a, Necmi Altin b,*, Ibrahim Sefa b
a
b
Vocational School of Technical Sciences, Gazi University, Ostim, Ankara, Turkey
Department of Electrical-Electronics Eng., Faculty of Technology, Gazi University, 06500 Besevler, Ankara, Turkey
article info
abstract
Article history:
In this study, a maximum power point tracking DCeDC quadratic boost converter for high
Received 14 November 2016
conversion ratio required applications is proposed. The proposed system consists of a
Received in revised form
quadratic boost converter with high step-up ratio and fuzzy logic based maximum power
28 January 2017
point tracking controller. The fuzzy logic based maximum power point tracking algorithm
Accepted 26 February 2017
is used to generate the converter reference signal, and the change in PV power and the
Available online xxx
change in PV voltage are selected as fuzzy variables. Determined membership functions
and fuzzy rules which are design to track the maximum power point of the PV system
Keywords:
generates the output signal of the fuzzy logic controller's output. It is seen from MATLAB/
Quadratic boost converter
Simulink simulation and experimental results that the quadratic boost converter provides
MPPT
high step-up function with robustness and stability. In addition, this process is achieved
Fuzzy logic controller
with low duty cycle ratio when compared to the traditional boost converter. Furthermore,
PV system
simulation and experimental results have validated that the proposed system has fast
response, and it is suitable for rapidly changing atmospheric conditions. The steady state
maximum power point tracking efficiency of the proposed system is obtained as 99.10%.
Besides, the output power oscillation of the converter, which is a major problem of the
maximum power point trackers, is also reduced.
© 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Introduction
PV based electricity generation is increasing exponentially
every year because of some reasons such as reduction of fossil
fuel reserves, negative effects on environment of fossil fuels
and increasing awareness on environment. In parallel to this
development, the researchers are concentrating on developing new and advanced PV system technologies [1,2]. Mainly
studies are focused on two major topics in order to obtain the
maximum benefit from the PV system investment. The first
research topic is designing new, high efficiency and low cost
PV cells and modules, and contains studies on modules
structure and materials. The second research topic covers
studies on power electronics converter topologies and their
control techniques. Different converter topologies and control
strategies have been proposed for this aim [1e9].
PV panels generate specific power at certain operation
conditions. The PV output voltage and current vary with
environmental effects such as the solar irradiation, the
ambient temperature, the pollution of the PV module surface,
shadowing etc. As it is known, environmental conditions vary
seasonally and on a daily basis. If these parameters change,
also the amount of produced power changes. Therefore, the
* Corresponding author. Fax: þ90 312 212 13 38.
E-mail addresses: sabanozdemir@gazi.edu.tr (S. Ozdemir), naltin@gazi.edu.tr (N. Altin), isefa@gazi.edu.tr (I. Sefa).
http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
0360-3199/© 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
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PV output parameters, according to the changing environmental conditions, must be continuously monitored. In
addition, the generated power form a PV module is also
related with the load level. Consequently, the PV module has
nonlinear power versus voltage (PeV) and current versus
voltage (IeV) characteristics and there is a unique operation
point on these characteristics that provide the possible
maximum power. This point is called as maximum power
point (MPP) [1,2]. Since, the load level of the practical PV system and environmental conditions such as solar irradiation
and ambient temperature continuously change, a proper
control algorithm and power converter should be used to
control the operation point and to keep the system at MPP.
This control algorithm is called as maximum power point
tracking (MPPT) algorithm. Many algorithms have been suggested for MPPT action [2e8]. In some studies, these control
algorithms are grouped as online and offline methods. Some
researchers group these methods as seeking and true seeking
methods, while others group them as direct and indirect
methods. The fundamental difference between these two
groups is related to performing way of the MPPT process [3,4].
Online methods measure some parameters form the PV system continuously and control the system to track the
maximum available power according to these measured data.
Besides, offline methods use some predefined formulas,
measurements or tables to perform same action. Although
offline methods are usually fast, online methods perform
more realistic MPPT process than offline methods. The most
well-known online methods are incremental conductance (IC)
and perturb and observe (P&O) techniques [1e4]. The fuzzy
logic control based methods are the new approach, and they
have become popular in recent years. Among the different
intelligent controllers, fuzzy logic controller (FLC) stands out
with its simple structure [5]. Furthermore, other artificial intelligence methods such as artificial neural networks, genetic
algorithms, particle swarm optimization are also used in
MPPT studies [6]. Soft switching MPPT converters are also
proposed to achieve higher total system efficiency [9].
The output voltage of the PV panel is usually lower than
required in typical energy applications such as motor drives,
inverters, etc. Therefore, this voltage level must be increased
for these type of applications. This required higher voltage
level may be accomplished by series connection of PV panels.
However, the number of series-connected PV panels must be
within certain limits in practice due to some limitations such
as PV voltage isolation, efficiency, shadowing effect, etc. On the
other hand, conversion ratio of the conventional DCeDC converters is usually not suitable for the required voltage level of
the aforementioned inverters for PV applications. (The conversion ratio is defined as the ratio of the output voltage to the
input voltage.) Moreover, it is well known that, in conventional
DCeDC boost converters, increasing duty cycle decreases the
stability and increases the control difficulty. Therefore, while
output voltage of boost converter increases exponentially with
duty cycle, in practice the voltage conversion ratio between
output and input voltage of the converter is recommended to
be selected as a maximum four [10]. Although, another alternative to increase the conversion ratio is using the isolated
DCeDC converter topology, this structure causes some problems such as cost, complexity, etc. [11]. Different DCeDC
converter topologies with high voltage step-up capability have
been investigated. The combination of the conventional boost
converter with switched capacitors have been proposed to
provide high conversion ranges. In this system, the output
voltage level is related to the number of capacitors used in the
circuit. However, voltage regulation action decreases the efficiency of the converter dramatically. Therefore, this topology
is suitable and provides high efficiency, if an additional converter is used for voltage regulation [12]. In addition, the power
switch suffers from high charge current. The DCeDC multilevel boost converter topology is proposed to overcome this
drawback. This topology also combines the boost converter
and switched capacitor action. The boost converter charges
several capacitors in series with its output (same) voltage [13].
Thus, output voltage can be easily controlled with a number of
series connected capacitors. Although this structure is very
suitable to supply neutral point clamped multilevel inverters,
the requirement that output capacitors should provide the
whole load current limits its usage.
The coupled-inductor technique is also used to obtain high
step-up converter [14]. However, the efficiency of this technique is low, and the leakage-inductor energy of the coupled
inductor will cause voltage spike on the switch and increase
switching losses [15]. Active and passive clamp circuits are
utilized to recycle the leakage inductor energy, but clamp
circuits increase the cost of the system. High step-up voltage
gain can also be achieved with two cascaded boost converters.
But this topology requires two controllers and two active
switches. The quadratic boost converter (QBC) which is
structurally similar to cascaded two boost converters has been
proposed to provide high voltage conversion ratio. The QBC
circuit is given in Fig. 1. The output voltage of the QBC is given
as a quadratic function of the duty cycle of switching signal
[16]. Since the QBC has only one active switch, additional
driver circuit requirement is removed and more reliable and
efficient converter is obtained. Therefore, the QBC is used in
several applications where high voltage conversion ratio is
required such as power factor correction applications and PV
applications [17e19]. The output voltage of the fuel cell or PV
module is usually low, and this low voltage should be
increased to supply conventional AC loads or to export
generated energy to the grid. Therefore, a robust, reliable and
high efficiency converter design with high voltage conversion
ratio is an important requirement. Although some studies
have been presented on QBC control, the number of studies on
MPPT quadratic boost converters is limited and a few simulation studies have been proposed [17,20,21].
In this study, a DCeDC quadratic boost converter with
MPPT capability for PV systems which requires high voltage
Fig. 1 e The PV supplied quadratic boost converter.
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
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step-up ability is proposed. The proposed converter contains
high conversion ratio QBC and the FLC based MPPT algorithm.
The FLC has two inputs and one output. The change in output
power (dP/dt) and the change in output voltage (dV/dt) of the
PV system are selected as inputs of the FLC, and change in
duty cycle is selected as the output variable of the FLC.
Therefore, the FLC based MPPT algorithm uses principles of
the IC algorithm, but it has adaptive nature and variable step
size advantages. The duty cycle of the QBC is obtained by
integrating the output of the FLC based MPPT algorithm.
Hence, the operation point of the quadratic boost converter is
adjusted according to the PV system parameters. Results of
MATLAB/Simulink simulations and experimental studies
show that, the QBC provides high voltage conversion ratio
even with low duty cycle values, and ensures robust and
stable operation. Additionally, the simulation and experimental results indicate that, the MPPT efficiency is obtained
as 99.10% and the oscillation of the converter output power at
the MPP is very small. Besides, the proposed converter has fast
response and reaches the MPP in 180 ms. It is seen that, the
proposed converter and MPPT method is convenient for
quickly changing atmospheric condition.
The quadratic boost converter
Quadratic converters can be implemented as buck or boost
type. In the past literature, it is possible to see a variety of
these structures [22e24]. Also some control algorithms are
3
proposed in recent studies [25e27]. The quadratic converter is
implemented by two series-connected converter with elimination of the second switch. Although the conversion ratio of
the converter is same as the cascaded converter, the number
of components and system cost are lower than the traditional
cascaded converter. However, it can be noted as a drawback
that, the efficiency of this converter is lower than the conventional buck or boost converter [22]. But, if the conventional
DCeDC boost converter voltage gain increases, which means
that it requires high duty cycle level, then the efficiency of the
converter will drop dramatically. In addition, the voltage
stress on the switch will also increase [28,29]. Furthermore,
high conversation ratio with the conventional converter causes electromagnetic interference due to the high duty cycle
level [30]. So, the QBC seems better solution for high step-up
applications. The voltage stress on power switch and efficiency values of the QBC are within the acceptable levels due
to lower duty ratio values while the voltage conversion ratio is
high. The schematic diagram of the quadratic boost converter
is shown in Fig. 1. As seen from the converter structure, it
looks like to two cascaded boost converters.
The circuit analysis can be explained according to Fig. 1. If
the controlled switch S1 turned on (ON state), then D1 and D3
diodes pass to the OFF state. In this situation, input supply
current flows L1 and D2. In this condition inductor L1 gathers
energy from the power supply and inductor L2 gathers energy
from capacitor C1. At the same time, load supplied by the
output capacitor C2. After that, the controlled switch turns off
(OFF state). In this condition, states of diodes are completely
Fig. 2 e The quadratic boost converter circuit a) When S1 is ON; b) When S1 is OFF.
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
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where u is the control signal which is “1” when S1 is turned on
(ON state), and “0” when S1 is turned off (OFF state). Here, i0 is
used as load disturbances. In steady state conditions, all the
derivative terms are equal to zero. Eq. (5) is obtained by
substituting the control signal D instead of u in Eqs. (1)e(4):
vc1 vc2
1
:
¼
¼
vpv vc1 1 D
(5)
Finally, the conversion ratio of the converter (M(D)) can be
obtained as given below:
Fig. 3 e The one diode model of the PV cell.
MðDÞ ¼
contrary; D1 and D3 are ON state, and D2 is OFF state. At the
same time, C1 and C2 capacitors are charged by L1 and L2 inductors, respectively. In addition, inductors supply the load
energy demand. Circuit diagrams for both operation conditions are depicted in Fig. 2.
The equation of the converter conversion ratio can be obtained from differential equations obtained according to the
control signal [31].
diL1 vpv vc1
¼
ð1 uÞ
dt
L1
L1
(1)
diL2 vc1 vc2
¼
ð1 uÞ
dt
L2
L2
(2)
dvc1
iL2 iL1
¼ þ ð1 uÞ
dt
C1 C1
(3)
dvc2
vc2
iL2
i0
¼
þ ð1 uÞ dt
Rload $C2 C2
C2
(4)
vc2
vc1
vc1
vpv
¼
1
ð1 DÞ2
(6)
As can be seen from Eq. (6) that, the conversion ratio of the
quadratic boost converter is quadratic expression and, this
ratio provides a high conversation ratio even though lower
duty cycle level is applied.
The PV model and MPPT algorithms
The PV cell converts sun light directly to electrical energy.
Different models and equivalent circuits for PV cell have been
proposed to investigate its performance for different operation
conditions. In addition, artificial neural network based models
have been proposed for PV systems [32,33]. Similarly, various
online and offline MPPT algorithms have been proposed for PV
systems to obtain fast and high accurate MPPT [1e7].
The PV model
PV cells can be modeled by a current source, a diode and a
high-value resistor connected in parallel to the current source,
Fig. 4 e Proposed fuzzy logic controller based MPPT quadratic boost converter.
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
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Fig. 5 e Basic illustration of the FLC.
and low-value resistance connected in series to the all circuit.
A diagram of this model called one diode model is given in
Fig. 3 [34]. In addition, the double-diode model is also common
in the literature.
According to the model, the following equation can be
written for the PV cell output current:
qðVout þ Iout $RS Þ
ðVout þ Iout $RS Þ
1 Iout ¼ IG I0 exp
n:k:T
RP
(7)
where, Iout is the cell output current, IG is the light-produced
current, I0 is the cell darkness current, q is the electronic
5
charge value (1,6.1019C), Vout is the cell output voltage, n is the
ideality factor, k is Boltzmann's constant (8.65 105 eV/K) and T
is the cell temperature (K). As can be seen from the equation,
the PV output current shows a nonlinear characteristic. Efficiency values of commercially available PV panels are around
9e21% according to their technology and materials [35]. This
efficiency value is only applicable at a certain current and
voltage values. The current and voltage values, that it is specific to the PV panel, are also related to atmospheric conditions such as temperature level, solar irradiation, etc. Because
of this nonlinear relation, the PV current and voltage, so the
output power should be continuously tracked to obtain
maximum energy conversion efficiency at different atmospheric conditions. Otherwise, maximum available power
cannot be get from the PV array.
MPPT algorithms
Many MPPT algorithms have been suggested in past studies.
Generally, offline methods estimate the maximum power
point (MPP) using some mathematical equations, measurements and look-up tables, etc. Performances and accuracies of
Fig. 6 e a) Membership functions of input variables b) Membership functions of the output variable.
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
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Table 1 e The rule base of the FLC.
Change in PV power (dP/dt)
Change in PV
voltage (dV/dt)
NL
NM
NS
Z
PS
PM
PL
NL
NL
NL
NL
Z
PS
PM
PL
NM
NL
NM
NM
Z
PS
PM
PL
NS
NM
NM
NS
Z
PS
PS
PM
Z
Z
Z
Z
Z
Z
Z
Z
PS
PM
PM
PS
Z
NS
NS
NM
PM
PL
PM
PM
Z
NM
NM
NL
PL
PL
PL
PL
Z
NL
NL
NL
these methods are related to many conditions, and usually
decrease with time because of some factors such as pollution,
aging, etc. Therefore, their efficiency values are lower than
online methods while their responses are fast. The most wellknown examples of offline methods are pilot cell (PC), lookup-table (LUT), constant voltage (CV) and constant current
(CV) methods. Online control methods have been performing
MPPT process more realistic than offline methods. The PV
voltage, current or power is continuously monitored and
questioned whether the operation point is the MPP or not. The
P&O technique, the IC technique, the ripple correlation control
(RCC) method, the current sweep technique (CST), the parasitic capacitance method common on line methods. Recently,
artificial intelligence based methods become popular such as
FLC, artificial neural networks, genetic algorithm, evolutionary algorithm, particle swarm intelligence, etc [1e4]. The
FLC provides high performance, even if the load and parameter changes and removes the system model requirement [36].
The main advantage of the FLC based methods over other
online control techniques is its lower dependency of the
mathematical model and system parameters. With this
feature, it has been reported to be more suitable for the MPPT
process especially for rapidly changing atmospheric conditions [37].
Most of these methods are effective under uniform insolation conditions. However, PV systems do not receive uniform insolation because of partial shading of PV modules. This
condition affects the PV system performance and PeV curve of
PV system of has multiple peak points (some local peak points
and a global peak point). The conventional methods do not
guarantee MPPT under non-uniform insolation condition,
thus some global maximum power point tracking methods
have been proposed [38].
Proposed quadratic boost converter with fuzzy
logic controller based MPPT algorithm
QBCs and their topologies have been studying for applications
require high voltage step-up. Although the FLC seems as a
mature method, application of the FLC for MPPT is still hot
topic and it is widely studied and discussed. In this study, the
FLC based MPPT technique is applied with the QBC to combine
advantages of high step-up nature of the QBC, and fast, robust
and stable operation ability of the FLC based MPPT method.
The block diagram of the proposed system including both
power and control systems is given in Fig. 4. The proposed
system consists of PV panels, the QBC and the FLC based MPPT
unit.
Basic structure of the FLC is shown in Fig. 5. As shown in
the figure, the FLC consists of a fuzzifier, an inference engine,
a knowledge base and a defuzzifier. The fuzzifier transforms
the crisp input data that obtained from the real word to linguistic labels and membership values by using the knowledge
base. After this process is completed, inputs are called fuzzy
inputs and used in fuzzy inference engine to generate verbal
judgments. The fuzzy inference engine uses the fuzzy inputs
and “IF e THEN” rules that are in knowledge base to generate
fuzzy outputs. The defuzzifier converts these fuzzy outputs to
crisp values. The knowledge base consists of the input and
output membership functions and rules which define the
relation between inputs and outputs.
In this study, a FLC with two inputs and one output is
proposed to track the MPP of the PV system. Input variables
of the FLC have been selected as change in the PV power
(dP/dt) and change in the PV voltage (dV/dt). The output
Fig. 7 e PV model parameters.
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
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Fig. 8 e QBC output characteristic according to the different irradiation level.
variable of the FLC has been selected as change in the duty
ratio (cD). The duty ratio of the QBC is obtained by integrating the output signal of the FLC. In one aspect, this
approach is similar to IC algorithm which determines the
MPP according to derivative of the PeV curve (at the MPP,
dP
dV ¼ 0), because of its input variables. However, its step size
is variable.
Input and output membership functions of the FLC are
given in Fig. 6. Seven membership functions are determined
for both input and output variables, and as it is seen, linguistic
labels such as positive large (PL), positive medium (PM), positive small (PS), zero (Z), negative small (NS), negative medium
(NM), negative large (NL) are used for these membership
functions. The rule base has been determined as given in
Table 1 to obtain fast tracking speed and reduced oscillations
at steady state. The minemax inference method, which it is
widely used and well-known method applied in this study. In
this method, the minimum (min) operator is used as fuzzy
implication function and fuzzified inputs are combined to
obtain rule strength. The maximum (max) operator is used to
combine outputs of the rules. The center of gravity defuzzification method which is given in Eq. (8) is used to obtain crisp
values of the output:
P
mðzÞ:z
:
z* ¼ P
mðzÞ
(8)
The center of gravity method calculates the center of
gravity of the area determined by the fuzzy output. Its output
varies continuously while inputs of the fuzzy logic controller
is varying continuously.
Simulation and experimental results
The proposed fuzzy logic controller based MPPT quadratic
boost converter is modeled and simulated in MATLAB/Simulink program. The proposed system consists of the PV array,
the quadratic boost converter and the fuzz logic controller
based MPP tracker. Sharp ND-167U1 type PV panel is used in
this study. The maximum power point voltage, current and
power of this panel at 25 C are given as VMP ¼ 23 V, IMP ¼ 7.27 A
and PMP ¼ 167.21 W, respectively. The PV array consists of two
strings and each one has 3 series-connected PV panels. So,
1 kW total PV array power is provided. All parameters of the PV
panel are given in Fig. 7. The figure also shows the IeV and the
PeV curves of the PV panel for two different temperature
levels.
The proposed fuzzy logic controller based MPPT quadratic
boost converter is simulated in different solar irradiation
conditions to test the performance of the proposed MPPT
converter system. The variation of the solar irradiation level is
given in Fig. 8(a). It can be seen that figure, irradiation level is
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
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Fig. 9 e PPPE interface program, the PeV curve of one string of the PV plant and the operation point.
changed 1000 W/m2 to 500 W/m2 at 0.15 s. Then, irradiation
level is ramped up to 1000 W/m2 level with a specific positive
slope. After that, the irradiation level is stepped-down to
500 W/m2 level at 0.45 s, and stepped-up to 1000 W/m2 level
again at 0.6 s. Afterward, the irradiation level is ramped-down
to 500 W/m2 with a negative slope. Finally, the irradiation level
is stepped-up to 1000 W/m2 level at 0.9 s.
The FLC based MPPT algorithm calculates the required
change in duty cycle to track the MPP of the PV system. The
duty cycle of the QBC converter is generated by integrating the
Fig. 10 e Variations of the PV voltage (Ch. 1), the switching signal (Ch. 2), output voltage (Ch. 3), the PV current (Ch. 4), and the
PV power (Ch. Math).
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
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Fig. 11 e The performance of the proposed system at startup.
output of the FLC. The output power, voltage and duty cycle of
the QBC are given in Fig. 8(b)e(d), respectively. As it is seen in
Fig. 8(b), the output power of the QBC tracks the variation of
solar irradiation which is directly related producible PV power
level. This figure also indicates that the FLC based MPPT algorithm has good performance not only under slow changing
and steady state conditions but also rapidly changing atmospheric conditions. It can be easily seen from Fig. 8(c) that, the
ripple of the QBC output voltage is very low, and thus the
power oscillations are also very limited. There is no unexpected behavior in this figure such as voltage spike, etc.
Fig. 8(d) indicates the duty cycle of the QBC. The duty cycle
always changes in order to track maximum power point of the
PV array according to the solar irradiation level.
The proposed FLC based MPPT quadratic boost converter
is also implemented and experimental studies are performed.
New generation SiC Mosfet (CAS300M12BM2 SiC based
MOSFET module) is used in QBC circuit as an active switch. In
addition, TMS320F28335 DSP is used to implement the FLC
based MPPT algorithm and to perform analog to digital conversions and PWM generation. The experimental set up of
the proposed system is supplied by MAGNA-POWER TDS III
600-8 model PV Power Emulator (PPPE). This PPPE is a DC
power supply which can act like PV modules. In this study,
the PPPE is programmed to operate as a PV plant which has
two strings each has 3 series-connected Sharp ND-167U1
type PV panels through its interface program by using parameters given in datasheets such as Voc, Isc, Imppt, Vmppt, Tn,
Tw, operation irradiation level, catalogue irradiation level,
irradiation coefficients, temperature, etc. In this study, two
parallel connected PPPE are used to emulate the PV plant and
supply the system (the interface program shows values of
one string). Thus, same PV plant (around 1000 W) is used in
both simulation and experimental studies. The PPPE generates a PeV curve according to the programmed conditions,
and changes its output current and voltage according to
defined PV characteristic. The irradiation and temperature
levels can be changed with computer interface during the
operation, and thus, different atmosphere conditions can be
tested.
The interface program of the PPPE is given in Fig. 9. The
parameters of the used PV module, the PeV curve of one string
of the PV plant and the operation point of the system for
1000 W/m2 irradiation level are shown in the figure. It is seen
that the proposed FLC based MPPT algorithm tracks the MPP of
the PV system and energy conversion efficiency of the PV
system is improved. The MPPT efficiency is obtained from
interface program as 99.10%. This value is higher than the
values presented in literature such 98.84, 98.78, 97.12, 95.00%,
and 90.8% [1,2,39e41].
In addition, the load and the solar irradiation levels are
changed to test the dynamic performance of the proposed FLC
based MPPT algorithm. The switching signal, the PV voltage,
the PV current, the output voltage and the PV power waveforms for this transition are shown in Fig. 10. It is seen that,
the proposed MPPT quadratic boost converter has fast
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
10
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
Fig. 12 e The PV voltage (Ch. 1), the switching signal (Ch. 2), output voltage (Ch. 3), the PV current (Ch. 4), and the PV power
(Ch. Math) waveforms, a) for 500 W power level b) for 1000 W power level.
transient response and reaches the MPP in about 180 ms. The
transient response of the proposed system is also visualized
by PPPE interface program. The PeV curve of the PV system for
actual solar irradiation level, the MPP and operation points of
the system, the PV voltage, PV current and the PV power
waveforms can be seen and tracked form interface program
screens. The PPPE interface program screen for the startup
moment is given in Fig. 11. As it is seen that, the proposed
system determines and tracks the MPP of the system fast, and
steady state oscillations are removed. Thus high efficient
MPPT algorithm is obtained.
Also the switching signal, the PV voltage, the PV current,
the output voltage and the PV power waveforms for low power
(about 500 W) and high power (about 1000 W) operation
conditions are given in Fig. 12. As can be easily seen from
figures, the proposed system is stable for both operation
conditions.
Furthermore, screen of the PPPE interface program for
variation of the solar irradiation conditions are given in Fig. 13.
The solar irradiation level is changed from 500 W/m2 to
1000 W/m2 and vice versa periodically. The PeV curve of the
PV system for actual solar irradiation level, the MPP and
operation points of the system for both two irradiation levels,
the PV voltage, the PV current and the PV power curves are
depicted in the PPPE interface program screen. This test also
prove that the proposed system has fast transient response,
stable steady state operation characteristics. As it can be seen
that, the power oscillation around the MPP is greatly reduced.
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
11
Fig. 13 e The performance of the proposed system for change in solar irradiation level.
Conclusions
In this study, a quadratic DCeDC boost converter with MPPT
ability for high step-up ratio applications is proposed. The
QBC provides high voltage conversion ratio with lower duty
cycle lower than the conventional boost converters, and provide lower voltage stress and higher efficiency values. The
QBC is also more simple, reliable and highly efficient than the
cascaded converters because the second active switch is
eliminated. The proposed system also includes a FLC based
MPPT controller. The proposed FLC has two inputs and one
output. The output power and output voltage of the PV panel
are selected as input variables, and the change in duty cycle is
determined as output variable of the FLC. The duty cycle of the
QBC is obtained by integrating the FLC output. Results obtained from MATLAB/Simulink simulations and experimental
studies indicate that the QBC ensures robust and stable
operation with low duty cycle even though high step-up is
required. The MPPT efficiency for steady state operation
condition is obtained as 99.10%. The proposed FLC based MPPT
quadratic boost converter has fast transient response and
reaches the MPP in about 180 ms. Additionally, the FLC based
MPPT algorithm reduced the oscillation of the converter
output power at the MPP.
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Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191