International Journal of Electrical and Computer Engineering (IJECE) Vol.
No.
October 2025, pp.
ISSN: 2088-8708.
DOI: 10.
11591/ijece.
Practical specification of the speech universe of the maximum power point tracking controller based on the asymmetrical fuzzy logic: a dynamic behavior study of the photovoltaic system Ahmed Amine Barakate.
Sami Choubane.
Abdelkader Hadjoudja Laboratory of Electronic Systems.
Information Processing and Energetics.
Ibn Tofail University.
Kenitra.
Morocco
Article Info
ABSTRACT
Article history:
In this paper, we present a procedure for extracting data from a stand-alone photovoltaic (PV) panel to program a maximum power point tracking (MPPT) controller based on the fuzzy logic (FL) method, aiming to optimize the performance of the photovoltaic system.
Photovoltaic data acquisition enables the determination of the input and output speech universe for the MPPT controller using fuzzy logic.
This method adapts to nonlinear systems without requiring a complex mathematical model.
Additionally, it improves the performance of the photovoltaic system in both dynamic and steady-state To further enhance the methodAos efficiency, an asymmetric membership function concept is proposed based on the dynamic behavior study of the photovoltaic system.
Compared to the symmetric method, the asymmetric fuzzy logic controller achieves higher maximum power output and better tracking precision.
This technology is essential for maximizing photovoltaic panel efficiency, a key requirement as solar energy gains prominence as a clean and renewable energy source.
Received Aug 7, 2024 Revised Apr 2, 2025 Accepted Jul 12, 2025 Keywords:
Asymmetrical fuzzy logic Efficiency of photovoltaic Fuzzy logic controller Maximum power point tracker Stand-alone photovoltaic This is an open access article under the CC BY-SA license.
Corresponding Author:
Ahmed Amine Barakate Laboratory of Electronic Systems.
Information Processing and Energetics.
Ibn Tofail University Kenitra Kenitra.
Morocco Email: ahmedamine.
barakate1@uit.
INTRODUCTION
In recent years, concerns over greenhouse gas emissions and escalating fuel prices have intensified the demand for alternative energy sources.
Among these, solar energy is one of the most sustainable and inexhaustible resources.
However, due to the nonlinear variation of current (I) and voltage (V) characteristics of photovoltaic (PV) cells under different irradiation and temperature conditions, it is crucial to operate PV systems at specific points to extract maximum solar energy.
This process, known as maximum power point tracking (MPPT), ensures efficient energy utilization.
Various MPPT methods have been developed and implemented in previous studies .
Ae.
, including perturbation and observation (P&O), incremental conductance, fractional open circuit voltage, fractional short circuit current, and fuzzy logic (FL) techniques.
These methods offer high tracking accuracy but often face trade-offs between tracking speed and precision under varying insolation conditions.
Fuzzy logic is advantageous as it does not require a precise and complicated mathematical model and can handle highly nonlinear systems.
Consequently.
MPPT algorithms based on FL have attracted significant research interest .
Ae.
Recently, many MPPT techniques based on FL have been proposed in the literature.
Compared to conventional algorithms.
FL-based MPPT techniques demonstrate improved tracking performance, response time and power efficiency under fluctuating climatic conditions, such as Journal homepage: http://ijece.
ISSN: 2088-8708
temperature variations and shading.
However, the design considerations and the complexity of implementing FL-based MPPT techniques vary significantly, necessitating further investigation into their optimization.
PROPOSED PHOTOVOLTAIC SYSTEM
The proposed system, illustrated in Figure 1, offers an innovative solution for generating stable power from a photovoltaic panel.
It comprises a PV panel, a DC/DC static energy conversion unit .
Ae.
, a load, and a block for calculating the maximum power point (MPP), which controls the converter.
The DC/DC converter serves as an interface between the PV panels and the storage system .
, .
, regulating voltage and current to maintain a stable and optimal power output regardless of temperature and solar irradiance To ensure real-time maximum power generation, an intelligent MPPT system based on fuzzy logic enables the converter to adapt the panelAos power output to match the loadAos requirements .
Ae.
The MPPT algorithm determines the optimal duty cycle for the converter based on input parameters, ensuring efficient and stable power generation.
The maximum power output of a photovoltaic generator is heavily influenced by climatic conditions, with MPP varying proportionally with irradiation (G) and inversely with temperature (T).
The creation of the inference table plays a key role in controlling the fuzzy logic technique.
There are two types of inference tables: the first, presented in Table 1, is a symmetrical fuzzy logic controller (FLC) derived from the power curve as a function of PV voltage in Figure 2.
The second type is asymmetrical, based on an analysis of the photovoltaic panel's behavior under varying climatic conditions .
, .
Figure 1.
System block diagram Table 1.
Inference table for symmetrical FLC Figure 2.
P-V curve of a solar panel Int J Elec & Comp Eng.
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October 2025: 4355-4365
Int J Elec & Comp Eng
ISSN: 2088-8708
BEHAVIORAL STUDY AND CONFIGURATION
Characterization of the API156P200 photovoltaic panel Technical characteristics of the photovoltaic panel We simulated our API156P200 type photovoltaic panel with a static load of RS=60 at the output.
The panel's modularity and lightweight design make it well-suited for remote applications, including water pumping systems, domestic installations, and military use .
Additionally, the API156P200 panels can be easily connected in series or parallel configurations to meet varying energy demands.
The technical specifications of the API156P200 PV panel are presented in Table 2.
Table 2.
Technical characteristics of PV API156P200
Technical data
Maximum power for STC
Voltage at maximum power point
Current at maximum power point
Short circuit current
Open circuit voltage Series resistance Shent resistance Number of cells in series Number of cells in parallel
Diode ideality factor
ABBR
Pmax
Vmpp
Impp
Isc
Voc
RSH
Unit Value 200A3% Electrical study of the photovoltaic panel The maximum power output of a GPV photovoltaic generator is significantly influenced by variations in irradiance and temperature.
As shown in Figure 3, the photovoltaic panel responds to changes in these factors, demonstrating that power output and the maximum power point (MPP) vary proportionally with irradiance Figure 3.
and temperature Figure 3.
, .
Table 3 summarizes the calculated results of the electrical quantities of the photovoltaic panel.
By determining the maximum power at a given temperature and irradiance, we establish that each maximum power corresponds to a specific duty cycle D, derived from .
, which allows us to determine the inference rules of the fuzzy logic control.
ya =1OeOo 0 ycIyc with ycIycC = .
ycOycyycyyco2 ycEycoycaycu .
Figure 3.
Power and current characteristics of PV as a function of voltage .
at T=25 AC and various G and .
at G=1000 W/m2 and various T Practical specification of the speech universe of the maximum power point A (Ahmed Amine Barakat.
A ISSN: 2088-8708
T (AC)
Table 3.
Maximum PV powers and their duty cycles G (W/m.
Pmax (W) Ippm (A) Vppm (V) R0 (E) Configuration of the fuzzy logic MPPT command In this section, we present the steps involved in configuring the fuzzy logic control.
First, we present the calculator diagram, followed by an explanation of the data extraction process from the photovoltaic panel.
This process is used to generate the input table .
lope and its variatio.
and, subsequently, the inference table .
Calculator diagram The fuzzy Logic control system consists of a calculator for the slope (E) and its variation (CE), derived from .
, along with a fuzzy logic controller block.
The schematic of the proposed control system is presented in Figure 4.
It shows the computation flow of the slope and its variation based on the photovoltaic voltage V_PV and the current I_PV of the PV.
ya= ycE.
OeycE.
coOe.
OeycO.
coOe.
yaya = ya.
Oe ya.
co Oe .
Based on the values of E and CE received by the LF controller .
uzzy logic bloc.
, the latter determines the value of the duty cycle D to control the converter.
The PWM block, pulse width modulation, is implemented to generate a logic signal with a fixed frequency, while its duty cycle is digitally controlled.
The average output signal corresponds to the duty cycle.
Figure 4.
Calculator diagram MPPT Acquiring PV data to configure fuzzy logic control In order to configure the fuzzy logic MPPT controller, we carried out a study about the dynamic behavior of the photovoltaic panel under varying climate conditions.
In each scenario, we fixed the values of illumination and temperature, then recorded the corresponding slope E and its variation CE.
This process allowed us to create a comparative table of the data and to define the table of inference rules.
To perform this Int J Elec & Comp Eng.
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Int J Elec & Comp Eng
ISSN: 2088-8708
step of extracting data E and CE from the photovoltaic panel, it is necessary to maintain the inputs .
emperature and irradiatio.
and their corresponding duty cycle D fixed in Figure 5.
The values of the slope and its variation are then recorded in registers for subsequent data extraction.
Figure 5.
Photovoltaic system controlled by pulse generator To set the duty cycle (D), we replaced the output of the MPPT controller with a pulse generator that generates an appropriate command signal based on the proposed temperature and irradiance conditions, as outlined in Table 3.
The data analyzed constitutes a sample because the number of recorded data is very The data acquired from the registers at irradiance levels of 1,000, 800, 600 and 400 W/m2 at a temperature set at 25 AC, are categorized based on the sign of the variation in the slope CE and then sorted by slope E.
These data are presented side-by-side in Tables 4 and 5 to define the ranges of the input variables used in the membership functions for the fuzzification step.
Table 4.
Data (E and CE) for variations of the positive slope at T=25 AC
Slope E
G=1000
Variation of slope CE
04E-05
Slope E
G=800
Variation of slope CE
Slope E
G=600
Variation of slope CE
Slope E
G=400
Variation of slope CE
3,00
43E-05
79E-06
17E-05
Practical specification of the speech universe of the maximum power point A (Ahmed Amine Barakat.
A ISSN: 2088-8708 Table 5.
Data (E and CE) for variations in the negative slope at T=25 AC
Slope E
G=1000
Variation of slope CE
Slope E
G=800
Variation of slope CE
Slope E
G=600
Variation of slope CE
Slope E
G=400
Variation of slope CE
05E-05
Fuzzification Fuzzification is a preliminary step that determines the subsets or intervals of maximum variation allowed in the input variables.
The purpose of fuzzification is to convert the input variables into fuzzy or linguistic variables.
In our case, we have two input variables: slope E and the variation of the slope CE.
For more precise results we have designated seven, instead of five, intervals of the input variables called: large negative (NB), medium negative (NM), small negative (NS), zero (ZE), small positive (PS), medium positive (PM) and large positive (PB) .
Ae.
Figures 6 and 7 show the membership functions of the input variables fuzzy subsets deduced from Tables 4 and 5.
Figure 6.
Membership function of input variables E Figure 7.
Membership function of input variables CE Int J Elec & Comp Eng.
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October 2025: 4355-4365
Int J Elec & Comp Eng
ISSN: 2088-8708
Inference and defuzzification Inference is the decision stage because we establish logical relationships between inputs and outputs while determining the rules of inference.
Figure 8 defines the membership function of the output variable D.
A thorough understanding of the system is essential for developing such a controller.
Specifically, the input value is represented by two fuzzy functions with different degrees, and the output is defined by several Several methods can fulfill this task.
We have chosen the Mamdani method for fuzzy inference, using MAX-MIN operations, where the MIN operator is applied for AND the MAX operator for OR.
Based on these rules, an inference table can be drawn up as presented in Table 6.
Finally, it is necessary to carry out the inverse operation of fuzzification and calculate a numerical value understandable by the external environment from a fuzzy definition.
This process is known as defuzzification.
The table of inference rules obtained from the behavioral study is asymmetrical, in contrast to the one derived from the p=f.
curve, which is symmetrical.
The simulation results of these two methods will be presented in the next section for comparison.
Figure 8.
Membership function of output variables D Table 6.
Inference table for Asymmetrical FLC
SIMULATION AND RESULT
Simulation environment The complete architecture of the simulated system is shown in Figure 9, which provides an overview of the interconnection between the main functional blocks.
To evaluate the performance of MPPT algorithms based on symmetrical and asymmetrical fuzzy logic, a series of numerical simulations was carried out using the MATLAB/Simulink environment.
The simulated system includes a photovoltaic panel connected to a DC/DC converter Boost, controlled by an MPPT controller implemented using a fuzzy logic system.
Simulation results under varying conditions The performance of the MPPT algorithms was evaluated by analyzing their response in terms of maximum power point (MPP) tracking time and overall system power efficiency.
The simulations considered variations in environmental parameters, such as temperature and irradiance.
Initially, the system was tested at a constant irradiance of 1000 W/mA while the temperature ranged from 55 AC down to 5 AC.
The evolution of the output power for both the symmetrical and asymmetrical fuzzy logic controllers is illustrated in Figure 10.
Practical specification of the speech universe of the maximum power point A (Ahmed Amine Barakat.
A ISSN: 2088-8708 We can clearly see that the output power based on asymmetrical FLC is greater than that of symmetrical FLC, the latter presenting anomalies in terms of stability, especially at high temperatures.
Additionally, we carried out simulations at a temperature of 25 AC, with luminosity varying from 1000 W/m2 to 500 W/m2, as shown in Figure 11.
The output power evolutions of the two systems, based on the FLC symmetrical and the FLC asymmetrical methods, are presented in Figure 12.
The power behavior at a temperature of 25 AC, with irradiance changing every 200ms from 1000 W/m2 to 500 W/m2.
The output power generated by symmetrical FLC is lower than that generated by asymmetrical FLC, especially in the luminosity range from 700 W/m2 to 500w/m2 at steady state.
The performance of the system using symmetrical fuzzy logic is incomplete in terms of power, which highlights the superiority of the asymmetrical FLC method in terms of output power and stability, especially in low-light conditions, as shown in Table 7.
It is important to note that the asymmetric FLC system improves the efficiency level of the system, especially at high temperatures and low luminosities .
The asymmetrical mode generates higher and more stable output powers than the symmetric mode in different conditions .
limate of temperature and However, the asymmetrical mode is more suitable for use in warmer regions and areas with low Figure 9.
Architecture of the simulated PV system and fuzzy logic MPPT control scheme Figure 10.
Output power with a decrease in temperature for Symmetrical FLC and Asymmetrical FLC Int J Elec & Comp Eng.
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Int J Elec & Comp Eng
ISSN: 2088-8708
Figure 11.
Change of luminosity at 25 AC Figure 12.
Output power with a decrease of luminosity for symmetrical FLC and asymmetrical FLC Table 7.
Shows the average power and efficiency of each method under different climatic conditions Luminosity Temperature Pmax Pout FLC Asymmetrical Efficiency Asymmetrical Pout FLC Symmetrical Efficiency Symmetrical CONCLUSION In order to determine the input and output speech universe of the fuzzy logic-based controller, we carried out a study of the dynamic behavior of the PV system.
This allowed us to extract the data from the stand-alone PV panel and define the membership functions for the maximum power point tracking controller We compared the results of the Symmetric inference table, derived from the PV power versus voltage characteristic curve, with those of the asymmetric inference table, obtained from the PV behavioral This comparison led us to conclude that the asymmetric FLC method is more reliable in terms of power efficiency and stability .
%) under adverse conditions, such as high temperatures, low light, and Practical specification of the speech universe of the maximum power point A (Ahmed Amine Barakat.
A ISSN: 2088-8708
REFERENCES