ABID-KAIS

Electrical Engineering
Design and implementation of reconfigurable MPPT fuzzy controller for
photovoltaic systems
K. Loukil
a,b,
⇑
, H. Abbes
a,b
, H. Abid
a,c
, M. Abid
a,b
, A. Toumi
a,c
a
University of Sfax, National Engineering School of Sfax, Tunisia
b
Laboratory of Computer and Embedded Systems, CES-Lab, Tunisia
c
Laboratory of Sciences and Techniques of Automatic, Control & Computer Engineering, Lab-STA, Tunisia
article info
Article history:
Received 7 June 2019
Revised 26 September 2019
Accepted 7 October 2019
Available online 31 October 2019
Keywords:
Photovoltaic
MPPT
Fuzzy logic
Design
FPGA
abstract
Since, photovoltaic (PV) systems are currently very expensive, many scientific studies are being con-
ducted to maximize the power such systems deliver. The best solution suggested so far consists of inte-
grating the Maximum Power Point Tracking (MPPT) with the PV power systems. The present paper
proposes to use the fuzzy logic technique in the actual implementation of the MPPT controller. The sys-
tem includes a photovoltaic panel, a boost converter and an fuzzy logic controller. This system is
designed, executed and tested under variable environmental constraints and using several technologies.
A comparison between these different technologies is made. The findings of the experiments demon-
strate an efficient operation of the FPGA-based PV system.
Ó2019 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-
nd/4.0/).
1. Introduction
Over the past few decades, energy consumption has risen expo-
nentially, largely due to both the massive worldwide industrializa-
tion and the rapid growth in such sectors as transportation and
electricity generation. Faced with this high and continuous
demand for ever larger amounts of energy, humanity is still relying
heavily on relatively cheap fossil fuels such as coal, oil and natural
gas in an attempt to satisfy its prodigious energy needs. These
three dependable sources together provided close to 67% of inter-
national electricity output in 2006 [1].
Yet, such increasing production of energy comes with the two
main problems usually attendant upon the overuse of traditional
sources of energy. To begin with, the huge need for more power
generation puts a strain on these primary sources, thereby result-
ing in the gradual depletion of the planet’s exploitable reserves of
these elements. It is projected that these conventional sources will
be nearly exhausted in the near future and that guaranteeing
adequate supplies of these materials will become more and more
difficult. Once the oil and natural gas falling discovery trend
together with their upcoming production peak and terminal
decline thereafter [2] are factored in, the future does not look par-
ticularly bright. At the current rate of consumption, global proven
oil reserves are projected to be used up in less than 50 years. Sim-
ilarly, the dwindling production rates will certainly cause oil
exploitation costs and end consumer prices to rise steeply. Recent
international oil crises like the one that took place in 2008, com-
plete with skyrocketing barrel prices and geopolitical disturbances,
are but a token preview of what the situation might be like in the
years and decades to come.
Furthermore, leaving aside the extraction and economic wor-
ries, conventional sources of energy are causing widespread envi-
ronmental destruction. Used mainly as combustibles, these
hydrocarbonic sources of energy generation are aggravating the
ecological situation of the planet as their escalating consumption
is leading to higher levels of greenhouse gas emissions and to an
increase in the presence in the air of poisonous pollutants such
as nitrogen oxides, sulfur dioxide, volatile compounds and heavy
metals. Acid rain, global warming, ozone layer holes, air pollution
and climate change are a few examples of phenomena that are
exacerbated by this economic trend. This accelerating environmen-
tal degradation has been going up at such an alarming speed that it
is threatening humanity’s own existence because of the severe
https://doi.org/10.1016/j.asej.2019.10.002
2090-4479/Ó2019 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
⇑
Corresponding author.
E-mail address: [email protected] (K. Loukil).
Peer review under responsibility of Ain Shams University.
Production and hosting by Elsevier
Ain Shams Engineering Journal 11 (2020) 319–328
Contents lists available at ScienceDirect
Ain Shams Engineering Journal
journal homepage: www.sciencedirect.com

damage fossil fuel consumption is dealing to the only known pla-
net that is fit for human habitation.
Due to this malignant scarcity-pollution dyad, clarion calls have
been made to wean the world off such polluting and disappearing
sources of energy and to seek for viable, eco-friendly substitutes.
Such a transition towards non-polluting renewable energies is con-
sidered a twofold solution to both problems mentioned above. Not
only are renewables essentially inexhaustible and broadly avail-
able resources that are expected to meet the growing demand for
energy, but they are also in harmony with a global trend to protect
the environment and shield the planet against the adverse effects
of the current energy generation situation. Attempts to tap such
renewable sources of energy as the wind, sun, water, waste and
biomass, while far from edging fossils out entirely, have the aim
of reducing the current dependency on conventional polluting
sources.
Owing to its abundance and widespread presence, the sun is
considered one of the most promising renewable sources of
energy. Solar energy is primarily harnessed via a photovoltaic sys-
tem. Primarily composed of photosensitive cells, solar, or photo-
voltaic (PV), panels form the basic component of any such
system. A PV panel has an important characteristic which is that
it is non-linear and has a particular point called the Maximum
Power Point (MPP). This MPP represents the optimum operating
point at which the panel operates at its maximum power. How-
ever, the photovoltaic energy produced is highly dependent on
the irradiance, the temperature and load, which impacts the posi-
tion of the MPP variable over time. This poses a serious challenge
to keep the production at its maximum possible all the time.
To meet this challenge, a number of published works propose a
variety of specific commands for the pursuit of the MPP, usually
referred to collectively as the Maximum Power Point Tracking
(MPPT). In the literature on the topic, several studies relating to
the comparison of the MPPT algorithms have been undertaken
such as [3–8,19,21,22]. These commands are generally discussed
while considering several factors including simplicity, speed of
convergence, cost etc. The Perturb and Observe (P&O) and Incre-
mental Conductance (INC) algorithms are the most frequently used
algorithms in photovoltaic systems thanks to their simple imple-
mentation [24,25]. However, these algorithms use a fixed pertur-
bation step to reach the optimal point, and have some other
shortcomings such slow convergence to the optimal point as well
as significant oscillations around it. Various approaches have been
developed to overcome these limitations [9,10,20].In[11], Huang
and Ren, develop a command that adjusts the perturbation step;
if the operating point is located in the right part of the PV charac-
teristic, the variable step is equal to a single step. If the operating
point is to be found on the left side, then the variable pitch is four
times that of the right side. This method reduces the oscillations
around the MPP. Nevertheless, when the MPP is reached, the vari-
ation of the pitch must be the same for both cases. Wang and Zhou
[12] propose a new algorithm that optimizes the selection of the
variation step. Their method is based on a multi-level step and uses
multiple parameters. It improves the speed of convergence
towards the optimal point and reduces oscillations. The major dis-
advantage of this method lies in the fact that the choice of these
parameters requires very complex calculations and a high degree
of accuracy.
Fuzzy Logic (FL) is now considered a promising solution to
resolve complex problems in a relatively simple way and without
the need to model the system. In particular, and as opposed to con-
ventional controls, FL control is considered a more elegant and
effective answer to the issue of non-linear systems tuning. With
this end in view, in [13] Won et al. use the concept of FL in the con-
trol of PV systems. The authors detail the operating principle of the
MPPT fuzzy algorithm, then they highlight the contribution and
performance of the algorithm he suggests in comparison to the
classic Hill-Climbing algorithm. Likewise, Alajmi [14] proposes
another FL algorithm where he employs the HiIl-Climbing algo-
rithm. The experimental results demonstrate that the FL algorithm
that he develops offers a faster and more precise convergence
towards the optimal point following a variation of the climatic con-
ditions. Much in the same way, the present work adopts the FL
concept to develop a new MPPT algorithm which both allows the
achievement of better performances and compensates for the lim-
its of classical algorithms. It aims to have better results and to
reduce complexity of trendy techniques such as [23,28,29]. The
operation of the entire system is tested through the simulation of
all its functions by means of efficient simulation tools.
In addition, heuristic algorithms and the popular particle
swarm optimization algorithm (PSO) have been introduced to
improve MPP tracking quality and to resolve some complex prob-
lems of conventional algorithms. However, high computational
requirement is the major drawback.
Recently, Priyadarshi et al. [30] have proposed an intelligent
fuzzy particle swarm optimization. Experimental results prove that
the proposed algorithm reaches MPP with zero oscillation, accurate
dynamic response and small convergence computational time.
Likewise, in [31], authors employed FPSO-based MPPT algorithm
to obtain best optimized solution. Experimental results show an
efficient power tracking of the hybrid FPSO and SVPWM inverter
control.
In [32], authors have developed a Jaya MPPT algorithm which
accelerates tracking ability with zero deviation and ameliorates
search performance. Besides, ultra capacitor is added to provide a
fast dynamic response by absorbing delivering power fluctuations.
In [33], a modified sine–cosine optimized MPPT is developed to
reach a rapid search of MPP without oscillations in steady state.
Traditional photovoltaic systems have limitations which bring
about problems of disparity between the photovoltaic modules.
Disparity is largely caused by the effects of shadows, clouds, dust,
falling leaves, etc. As a result, the total power of the chain of PV
panels decreases when a single PV panel is affected by any of these
hindering factors. The new architecture implemented by multi-
channel PV systems facilitates the extraction of the optimal oper-
ating point for each module and eliminates losses due to disparity.
The control can be either distributed, i.e. each PV module is associ-
ated with its converter which is in turn controlled by either a local
control unit or by a central one whose functioning principle con-
sists in the gathering of the local units into a single unit to control
the PV panels.
The control system is a major challenge in multi-generator PV
systems. So far, MPPT control has been implemented using micro-
controllers and Digital Signal Processors (DSPs). Nonetheless, this
type of implementation does not present an effective solution to
the control of a multi-generator PV system. In recent years, there
has been a growing trend towards the use of Field Programmable
Gate Array (FPGA) in such a system as in [26,27]. This type of tech-
nology allows the integration of multiple photovoltaic generators
that take a very short space of time to execute. A comparative
study is detailed in the present paper with the aim of choosing
the appropriate technology for this type of PV system.
The remainder of the paper is organized as follows: in section
two; the photovoltaic system considered to be studied is mod-
elized and described. The third section schematizes the system
design and delineates how the simulation of the photovoltaic sys-
tem is to be carried out. In section four, the implementation is per-
formed of the MPPT fuzzy controller using different technologies.
Lastly, some concluding remarks are drawn and included in the
fifth and final section of the paper.
320 K. Loukil et al. / Ain Shams Engineering Journal 11 (2020) 319–328

2. Photovoltaic system modelling
As shown in Fig. 1, a photovoltaic system includes four blocks:
(a) a solar array delivering electric energy, (b) a DC/DC converter
which connects the PV panel to the load, (c) the load, and (d) the
MPPT control unit [15]. The lead role of the static converter is to
make an impedance matching in such a way that the panel gener-
ates its maximum energy.
3. Proposed MPPT algorithm
In this section, a new fuzzy MPPT algorithm is proposed. First,
the original idea born is explained. Second, the operation is
detailed. Finally, the algorithm is designed.
A. MPPT algorithm concept
In order to have an efficient performance of the photovoltaic
system, there must be optimal MPPT control. Within this frame-
work, a new algorithm is proposed that is a synthesis of the classi-
cal algorithm incremental conductance and FL. The suggested
algorithm is inspired by the simplicity of the INC algorithm to pro-
duce a new fuzzy algorithm that is tentatively called INC-Fuz and is
supposed to enable the surmounting of the INC algorithm draw-
backs identified below.
The INC algorithm is popular, simple and easy to implement.
However, it is plagued by such limitations as:
[1] low convergence speed to reach the optimal point
[2] significant oscillations around the MPP in steady state
[3] momentary system movement away from the MPP follow-
ing any rapid or sudden change in irradiation
These drawbacks of the INC algorithm are illustrated in Fig. 2
below:
The main cause of these disadvantages is the use of a fixed step
to reach the optimal point. To accelerate the searching process, a
bigger step must be used despite the fact that, in a steady state,
it is more efficient to use a small step. The idea in the present paper
is to apply a variable step instead of a fixed one. Such an incremen-
tal step is large at the beginning of the process, average at its mid-
dle and small in the stable state. This step is generated from a block
based on fuzzy logic.
The proposed INC-Fuz algorithm works in the following way:
At the beginning, the input variables I(k) and V(k) are measured
(k is the time instant); after that, the fuzzification stage takes place,
and then decisions are made in the inference stage. A variable step
is then generated from this fuzzy block. Thus, testing the sign of
the ratio dP/dV determines the value of the duty ratio ‘‘D”.
The diagram of the INC-Fuz algorithm is shown in Fig. 3.
B. MPPT algorithm operation
As detailed in [16], the principle of the INC algorithm is that it
considers the value of the slope of the PV characteristic to deter-
mine the position of the operating point with respect to the opti-
mal point. This concept is adopted first to establish the equations
of two inputs E1 and E2 of the fuzzy system and, second, to pro-
duce the output signal defined by the variable step dD.
E1 ¼I
VþdI dV ð1Þ
and
E2 ¼E1 kðÞE1 k 1ðÞ ð2Þ
The value of the slope dp/(dV) may be negative to the right of
the optimum point, positive to its left and approximately zero
when in this optimal point’s region. Fig. 4 illustrates the P-V char-
acteristic that is spread over these three different regions.
Figs. 5 and 6 show a distribution of the characteristic P(V)
according to the value of the slope. Two external characteristics
(T = 5 °C and G = 1000 W/m
2
) and (T = 75 °C and G = 200 W/m
2
)
are scrutinized. In fact, the P-V characteristic changes as a result
Photovoltaic
panel
DC-DC
converter Load
MPPT
controller
Irradiation
Temperature
Fig. 1. Block schema of photovoltaic system.
Fig. 2. Disadvantages of the INC Algorithm.
K. Loukil et al. / Ain Shams Engineering Journal 11 (2020) 319–328 321

of changes in temperature and illumination. The value of the slope
consequently changes. For these reasons combined, these changes
are considered so as to put in place the intervals of slope variation.
Slope values are divided into five subsets: Zero (Z), Small Posi-
tive (SP), Small Negative (SN), Large Positive (LP), Large Negative
(LN).
C. Design of the proposed MPPT
The fuzzy subsets of input variables, shown in Fig. 7, are asym-
metric and condensed in the middle. This type of fuzzy subsets
provides greater sensitivity and more flexibility.
Each region is assigned a variable step. For region (Z), we assign
a small step that is equal to
a /6. For regions (SP) and (SN), we
assign an average value step that is equal to 2
a /3. A step of greater
value (
a ) is assigned for the regions (LP) and (LG).
A table of inference rules is established summarizing the oper-
ation of the new fuzzy system (Table 1). These rules are developed
based on the concept of the INC command.
In order to better understand how these rules operate, an exam-
ple of a rule is treated and explained:
If (E1 is LG) and (E2 is LG), then (d is
a )
This means that, if the slope has a large value (region 3) and the
slope change is also significant, then the operating point is far from
the MPP. Therefore, a large value is assigned to the duty cycle step.
The fuzzy method of Takagi-Sugeno (T-S) is considered to pro-
vide concrete value. A table of rules of inference is thus provided
below (Table 1).
4. Simulation of the INC-Fuz algorithm in Matlab/Simulink
The simulation results of the new MPPT algorithm in compar-
ison with the conventional algorithm are shown in Fig. 8.
Fuzzy block
Begin
Measure of
I(k), V(k)
Fuzzification
Inference
D=D-dD
D=D+dD
I(k-1)=I(k)
Variable step dD
dP/dV >0
Fig. 3. Diagram of INC-Fuz algorithm.
Fig. 4. Sign of the slope value on the P-V characteristic.
Fig. 5. Distribution of the P-V characteristic according to the value of the slope for
the pair (temperature, irradiation): (5 °C, 1000 W/m
2
).
Fig. 6. Distribution of the P-V characteristic according to the value of the slope for
the pair (temperature, irradiation): (75 °C, 200 W/m
2
).
322 K. Loukil et al. / Ain Shams Engineering Journal 11 (2020) 319–328

These results prove that the INC-Fuz convergence speed is
higher than that of the conventional algorithm and that the PV out-
put power has fewer oscillations around the MPP. In addition, the
new MPPT controller exhibits good behavior during a sudden
change in irradiation.
As a result, compared to the conventional fuzzy algorithm, the
proposed INC-Fuz algorithm has a simpler, more user-friendly
and less sophisticated design that yields better performance.
Finally, compensation for the three major disadvantages of the
INC algorithm is enabled through the use of the FL concept.
The impact of partial shading on PV sytem based on proposed
MPPT technique is shown in Fig. 9.
Fig. 9 decepited that PV system power changes smoothly with
the irradiation variations:
At the first state (25°C, 1000 W/m2), PV sytem reaches a max-
imum power equal to 81.4 Watt as an intended optimal power.
Then, at 25°C and 700 W/m2, PV system attains 48.6 Watt as an
intended optimal power. So, simulation results proves that under
changing weather PV sytem converges effectively and exactly to
its optimal power.
5. Implementation of the fuzzy controller on a FPGA circuit
In this section, fuzzy controller is implemented on FPGA. First,
the algorithm is developed on Quartus environment using VHDL
language. Second, it is simulated and results are compared and dis-
cussed. Third, the experimental structure of photovoltaic system is
Fig. 7. Fuzzy subsets for inputs E1 (a) and E2 (b).
Table 1
Inference Rules.
E2
E1 LG SN Z SP LP
LG Α2
a /3 2 a /3 2 a /3 a
SN 2 a /3 2 a /3 2 a /3 2 a /3 a
ZΑ a /6 2 a /3 a /6 a /6
SP 2
a /3 2 a /3 2 a /3 2 a /3 2 a /3
LP
a /6 2 a /3 2 a /3 2 a /3 Α
Fig. 8. Power and voltage of the improved INC-Fuz algorithm compared to those of the classical algorithm after the PV system startup.
Fig. 9. The impact of partial shading on INC-Fuz algorithm.
K. Loukil et al. / Ain Shams Engineering Journal 11 (2020) 319–328 323