Investigation of Artificial Intelligence Algorithms for MPPT of Solar
Photovoltaic System
MOHAMED I. ABU EL-SEBAH1, ALY M. EISSA2, MOHAMED FAWZY EL-KHATIB2
1Power Electronics and Energy Conversion Department,
Electronics Research Institute,
Cairo 11843,
EGYPT
2Mechatronics and Robotics Engineering Department, Faculty of Engineering,
Egyptian Russian University,
Cairo 11829,
EGYPT
Abstract:- Solar energy has gained prominence as a primary renewable energy source for the generation of
electricity in recent years. The maximization of power extraction from photovoltaic (PV) systems is a topic of
significant interest due to the relatively low conversion efficiency of these systems. Therefore, a maximum power
point tracking (MPPT) controller is essential in a PV system to achieve the desired output power. This paper
implements three different MPPT controllers: sliding mode control (SMC), fuzzy logic controller (FLC), and
artificial neural networks (ANNs). The performance of these controllers is evaluated on a PV system under varying
irradiation and temperature conditions to analyze their ability to track the maximum power point (MPP). The
results demonstrate that the SMC outperforms the FLC and ANN in terms of best performance with minimum
oscillation under different operating conditions.
Key-Words:- Photovoltaic system, Boost converter, Sliding mode control, Artificial Neural Networks, Fuzzy Logic
Controller, Simulation.
Received: March 19, 2023. Revised: December 4, 2023. Accepted: December 21, 2023. Published: December 31, 2023.
1 Introduction
In today’s world, there is a growing need, for energy
sources, like power, wind energy, and biomass to
meet the increasing electricity needs of consumers.
These sources are pristine, abundant, and pollution-
free. Photovoltaic (PV) is a prominent clean energy
source that efficiently powers electrical loads and may
also feed energy back into the utility system. MPP
operation is necessary because of the nonlinearity of
the PV system's output. To ensure the continuous
operation of MPP under varying ambient temperatures
and solar radiation conditions, the use of control
algorithms is necessary. It is difficult to determine
which MPP has the smallest oscillation in proximity
to the operational point, [1], [2], [3], [4].
Two ways to follow the MPP, the first is the
conventional methods such as Fractional open circuit
(OC) voltage, Fractional short circuit (SC) current,
Incremental conductance (IC), and Perturbation and
observation (P&O). The main limitation of most of
these earlier algorithms is their tendency to oscillate in
the vicinity of the operating point in a steady
state. Additionally, the direction of tracking the MPP
is disrupted due to swiftly changing atmospheric
conditions, [5], [6], [7], [8], [9].
The second is the intelligence methods such as
FLC and ANN, SMC, and others. The ANN method
must have undergone training using a significant
number of solar irradiance and ambient temperature
measurements. The FLC is sensitive to voltage
variations and the current output from the PV panel.
The system utilizes the derivative of power
concerning current (dP/dI) and its derivatives as the
inputs and calculates the duty cycle of the MPPT
converter, [10], [11], [12], [13]. The SMC uses a step
size that can be adjusted on the fly. When the
operational point is significantly distant from the
sliding surface, this method increases the step size to
WSEAS TRANSACTIONS on SYSTEMS and CONTROL
DOI: 10.37394/23203.2023.18.58
Mohamed I. Abu El-Sebah,
Aly M. Eissa, Mohamed Fawzy El-Khatib
E-ISSN: 2224-2856
561
Volume 18, 2023
expedite progress. Given that the effective point is
near the sliding surface, the system will experience
smaller step sizes, resulting in a shortened duration of
oscillation around the maximum power point (MPP),
[14], [15], [16], [17], [18]. The novelty can be
summarized as:
1. Developing and adapting the FLC, ANN, and SMC
based MPPT to optimize the power output of a PV
with a resistive load.
2. Tracking of the MPP under different operating
conditions during constant irradiance levels, rapid
changes in irradiance levels, and rapid changes in
temperature levels.
The organization of this paper will be as follows:
Section 2 introduces the photovoltaic system. Section
3 describes the boost converter design. Sections 4, 5,
and 6 present the MPPT controllers: SMC, FLC, and
ANN, respectively. Section 7 shows the simulation
results, while Section 8 presents the conclusion.
2 Photovoltaic System
The solar system under consideration, seen in Fig. 1,
employs the PV system, whose electrical properties
are listed in Table 1, a boost converter, a neural
network based MPPT controller, and a resistive load,
[19], [20], [21], [22], [23].
Fig. 1: Block diagram of the proposed system
Table 1. Parameters of PV module.
Module Characteristics
Values
220 W
30 V
7.35 A
8.81 A
36.8V
The single-diode variant strikes a good balance
between ease of use and precision. In Figure 2, we see
a photocurrent source connected in parallel with a
nonlinear diode, a shunt resistor, and a series resistor.
The photocurrent's origin is largely established by the
cell's operational temperature and the amount of solar
irradiation it receives. These equations characterize
the PV cell model, [24], [25], [26]:
(1)
(2)
(3)
Where Iphphotocurrent current, : diode current, I
output current from the cell, : shunt resistor
current, : saturation current of the diode, :
Boltzmann constant,electron charge, : actual
temperature of the cell, : shunt resistance, and :
short-circuit current: reference temperature,
irradiance and : series resistance, V and I are the
output current and voltage of the PV module
respectively.
3 Boost Converter Design
The following clause describes the modeling of the
dynamic behavior of the boost converter, [27], [28],
[29], [30]:
When the switch is on:
(4)
When the switch is off:
(5)
The dynamic characteristics of the boost converter are
from (6) and (7):
(6)
Where Vpv: is the PV array voltage, : the inductor
current Vo: the converter output voltage, and VL: is the
converter inductor voltage.
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4 Sliding Mode Control
SMC is a type of dynamic controller that is used to
create resilient controllers for complicated, high-
order, nonlinear dynamic plants that operate in
uncertain settings. There are two fundamental
concepts underlying SMC, the first creating a sliding
surface that will function as the operational point in
the initial stage. Secondly, within a finite amount of
time, establishing a control law that shifts the
effective point to a predetermined surface, [31], [32],
[33], [34].
Begin by developing a surface within state space.
The process of choosing a control law that
compels the system's state path to approach a
pre-established surface within a limited
period.
Sustaining it in proximity to this surface by
employing suitable switching logic.
The SMC dynamic analysis: sliding surface
proposition to be:
(7)
with G1 and G2 representing constant gains.
To implement the suggested method, (S) must
equal zero and dS must be:
(8)
(9)
(10)
from (9) and (10) we obtain the following equation
(11)
Substituting (9) and (10) in (6)
(12)
Substituting (3) in (6)
(13)
Equation (7) thus characterizes the SMC dynamic
model, in which the successful implementation of the
proposed method is ensured by the optimization of the
fixed gains and parameters for the boost converter.
5 Fuzzy Logic Control
By regulating the duty cycle of the converter, the FLC
targets to optimize the monitoring efficiency of the
MPP of the PV panel. The inputs of the FLC
correspond to the PV panel output current and
voltage, whereas the output of the controller signifies
the duty cycle. To optimize power output, the FLC
controls the voltage through the converter duty ratio,
which is determined by the power ratio (dp/dv)
change. The FLC is utilized to regulate the DC
converter and accepts duty cycle (CD), error (E), and
change in error (CE) signals as inputs. The parameters
for the input and output are as follows, [35], [36],
[37]:
(14)
(15)
(16)
Where the immediate power (P(k)) and instant
voltage (V(k)) of the PV are denoted as such.
The direction in which the load target point at
instant k is moving is denoted by the change in error,
while the input error E(k) shows whether the target
point of the load is to the right or left of the MPP of
the PV panel. The controller's design comprises three
fundamental components: defuzzification, base rule,
and fuzzification, [38], [39], [40], [41], [42]. The
input and output membership functions of the
controller are illustrated in Figure 2. The FLC rule
includes 25 rules, as described in Table 2.
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(a)
(b)
(c)
Fig. 2: Fuzzy system membership function. (a) E. (b)
CE. (c) CD
Table 2. Rules of the FLC
6 Artificial Neural Network
There are several ways in which AI methods excel
over more conventional methods. Traditional systems
have limitations including being slow to adapt to
shifting solar temperature and irradiance, and
occasionally failing to keep tabs on the peak power
point. Input data (such as irradiance and temperature)
is received by the input layer, and from there is sent to
the second and third layers via a network of hidden
neurons. The converter's output (duty cycle) is
supplied by the third layer. To determine the MPP, a
three-layer neural network is illustrated in Figure 3.
The input layer is responsible for receiving input data,
which consists of temperature and irradiation. Several
concealed neurons are located in hidden layers, from
which the input layer transmits data to the third layer.
The duty ratio output is supplied to the converter by
the third layer. The inputs and outputs are, [43], [44],
[45], [46], [47]:
(17)
(18)
Where and : The lines linking the three
levels are allocated weights;
and
: The bias
values associated with the concealed layer and output,
respectively; and
: The signal values about the
input and output lines.
Fig. 3: The construction of the ANN
7 Results and Discussion
The simulations utilized in this research were carried
out through the implementation of systems in
MATLAB, which execute the suggested SMC, ANN,
and FLC. The output PV power of the SMC, FLC,
and ANN using constant conditions (T = 25C, G =
1000W/m2) is depicted in Figure 4. The SMC exhibits
superior characteristics in terms of rise time, response
time, and absence of overshoot when compared to the
ANN and FLC, [48], [49], [50], [51], [52], [53].
E
CE
NB
NS
ZE
PS
PB
𝐍𝐁
PS
PB
NB
NB
NS
𝐍𝐒
PS
PS
NS
NS
NS
𝐙𝐄
ZE
ZE
ZE
ZE
ZE
𝐏𝐒
NS
NS
PS
PS
PS
𝐏𝐁
NS
NB
PB
PB
PS
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Fig. 4: An analysis of the three controllers in
comparison to a constant atmospheric condition
For the three controllers, Figure 5 depicts the PV
output power when the irradiance drops abruptly from
1000 W/m2 to 800 W/m2, then to 600 W/m2, and
ultimately to 400 W/m2. They possess an exceptional
capacity to monitor the MPP by the four controllers.
However, the rise and settling periods of the SMC are
shorter.
Fig. 5: The power output of the three controllers at a
constant 25 C and varying levels of irradiance.
The PV output power for all three controllers is
depicted in Figure 6, with temperature fluctuations
and irradiance of 1000 W/m2 held constant. The
temperature fluctuated between 25C and 30C, then
35C, 40C, and 25C once more. It is noteworthy that
they possess an exceptional capability to monitor the
MPP for every controller. However, the rise and
settling periods of the SMC are shorter.
Fig. 6: The power output of the three controllers as the
temperature and irradiance levels remain constant.
8 Conclusions
This paper describes various MPPT control algorithms
utilized to determine the MPP of a PV panel subjected
to variable conditions. The SMC, FLC, and ANN
were devised to monitor the MPP across diverse
atmospheric conditions. To assess the effectiveness of
the controllers being evaluated, a variety of scenarios
were investigated. These scenarios comprised
consistent irradiance levels, precipitous fluctuations in
irradiance levels, and abrupt temperature changes. In
contrast to FLC and SMC, the results demonstrate that
the SMC can monitor the reference rapidly and with
superior performance. At a steady condition, the MPP
is devoid of any oscillation.
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Contribution of Individual Authors to the Creation
of a Scientific Article (Ghostwriting Policy)
The authors equally contributed in the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare.
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