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DESIGN AND ANALYSIS OF A FUZZY LOGIC CONTROLLED PHOTOVOLTAIC
SYSTEM WITH A BIDIRECTIONAL DC-DC CONVERTER
Ergashov Kakhramon Abdukokhkhorovich
E-mail: qahramon.ergashov@list.ru,
Fergana state technical university
Abstract:
This paper presents a detailed model of a photovoltaic (PV) system integrated with a
bidirectional DC-DC converter, employing the Adaptive Neuro-Fuzzy Inference System
(ANFIS) for intelligent control. The model considers two key input variables: time and ambient
temperature. By incorporating ANFIS, the system adapts dynamically to environmental
fluctuations, enhancing its overall performance. Key performance indicators such as voltage
stability, current fluctuation mitigation, and battery charge optimization are analyzed to assess
system effectiveness. Simulations are carried out in the MATLAB/Simulink environment,
providing a robust framework for evaluating system behavior under varying operating
conditions. Results indicate that the integration of ANFIS significantly improves energy flow
management, enhances stability, and ensures a higher quality of power output. Furthermore, the
model demonstrates adaptability to changing external conditions, making it a viable solution for
real-world renewable energy applications and intelligent PV power management.
Keywords:
photovoltaic panel, fuzzy logic, fuzzy controller, ANFIS, MATLAB/Simulink
modeling, bidirectional converter, MPPT, renewable energy.
1. Introduction
The accurate modeling of photovoltaic (PV) systems is vital in advancing renewable energy
technologies. These systems are inherently nonlinear and highly sensitive to external
parameters such as solar irradiance and ambient temperature. Achieving optimal performance
requires not only the physical modeling of components but also the integration of advanced
control strategies and power converters.
Recent advancements in PV system research span various domains, including component-level
electrical modeling, intelligent control algorithm development, and forecasting techniques.
Several studies have addressed these aspects comprehensively.
For example, [1] examines how shading affects electrical mismatches in PV systems,
particularly highlighting the role of bypass diodes in reducing power loss and reverse voltage.
However, it notes inefficiencies under light shading, where current splits between the module
and diode. In [2], a support vector machine (SVM)-based algorithm predicts PV output based
on weather classification into four categories (clear, cloudy, foggy, and rainy). This model,
validated on a 20 kW system, achieved high accuracy in power forecasting, showing promise
for grid-connected applications. DC-DC converters are integral to PV efficiency. Study [3]
introduces a converter design utilizing a coupled inductor in both series and parallel modes,
yielding greater voltage gain than traditional configurations. In [4], an improved PI-controlled
boost converter model simplifies control circuit design while enhancing voltage regulation.
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Maximum Power Point Tracking (MPPT) remains a cornerstone of PV optimization. A fuzzy
logic-based MPPT method in [5] demonstrated 94.8%–99.4% accuracy across varying
irradiance (700–1000 W/m²) and temperature (25–60°C). Similarly, [6] presents a PV model
combined with a bidirectional energy storage system, validating the PI algorithm for effective
energy flow control. Study [7] explores solar radiation modeling using clearness index and
time-based diurnal patterns to refine converter duty cycles. In [8], bifurcation analysis uncovers
nonlinear instability phenomena, emphasizing the necessity for robust control schemes. These
findings affirm the centrality of converter control, intelligent methods (e.g., ANFIS, fuzzy logic
controllers, SVM), and advanced MPPT techniques in PV research [9]. Recent developments
further reveal the superiority of AI-based MPPT algorithms. Deep learning models, such as
LSTM, achieve up to 30% higher efficiency in variable light conditions compared to traditional
perturb and observe (P&O) and ANN methods [10].
Innovations in converter design are also noteworthy. For example, [11] introduces a three-phase
interleaved boost converter directly interfacing with Li-ion batteries, eliminating the need for
separate charging circuits. Adaptive strategies like ASGAO-RBFN not only improve MPPT
accuracy but also enhance converter efficiency [12]. Moreover, integrating PV and wind
systems into the grid calls for advanced energy management techniques. Study [13] shows that
battery systems with controlled reverse energy flows can stabilize operation and reduce losses,
especially in regions with limited infrastructure. Building upon this div of research, the
present study proposes a MATLAB/Simulink-based model of a PV system equipped with a
bidirectional DC-DC converter. The model employs the ANFIS algorithm to achieve high-
precision control in the face of variable external conditions, making it a promising candidate for
further development and real-world deployment.
2. Photovoltaic Network Modeling
Photovoltaic systems are complex and nonlinear by nature, with their performance strongly
influenced by environmental conditions such as solar radiation and ambient temperature. At
their core, PV cells convert sunlight into electricity and are organized into modules and arrays
to meet specific voltage and power requirements. Modern modeling approaches aim to reflect
the dynamic behavior of PV systems under real-world operating conditions. These include
considerations such as partial shading, non-uniform temperature profiles, component aging, and
the influence of inverters and other electronic components on system performance. Simulation
platforms like MATLAB/Simulink play a critical role in this process. They allow for detailed
system design, performance analysis, and the testing of various control strategies—most
notably MPPT algorithms. As a result, PV network modeling serves as a foundational tool in
optimizing energy conversion, improving system reliability, and supporting the integration of
renewable technologies into the electrical grid [14].
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Figure 1. Simulation of a photovoltaic panel with a DC-DC converter
The modeled photovoltaic system consists of the following key components: Photovoltaic Panel
(PV Panel), Electrical Load. Battery Storage, Fuzzy Logic Controller, Proportional-Integral (PI)
Controller, Measuring Instruments
1. Photovoltaic Panel (PV Panel): The photovoltaic panel converts incident solar radiation into
electrical energy. It provides a DC voltage output whose magnitude is influenced by
environmental conditions, particularly ambient temperature and solar irradiance over time. The
panel serves as the primary energy source for the system.
2. Bidirectional DC-DC Converter: The DC-DC converter manages power flow between the PV
panel, the load, and the battery. It functions in two operational modes:
Boost Mode: Increases the voltage from the PV panel to match the system’s power
requirements.
Buck Mode: Decreases the voltage to facilitate energy transfer to either the battery or the load,
depending on system demand.
3. Converter Components: Inductor (L): Smooths the current flow by limiting rapid changes,
thereby reducing current ripples. Capacitors (C1, C2): Filter the voltage to stabilize output
during switching operations. Switching Elements (Diodes and Transistors): Enable mode
transitions (boost/buck) through controlled switching actions. MPPT Algorithm: The Maximum
Power Point Tracking algorithm generates the control signals for converter switching. It ensures
that the PV panel consistently operates at its optimal power output point, enhancing efficiency.
4. Battery: The battery stores excess electrical energy produced by the PV panel during periods
of high generation. It discharges stored energy when generation is insufficient to meet the load
demand, ensuring system reliability and power continuity.
5. Load: The system supplies power to a connected electrical load. The load represents the real-
time energy consumption component and is powered either directly from the PV panel or via
stored energy from the battery.
.
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Figure 2. Initial PV data.
The calculation of the algorithm for modeling the photoelectric system is shown in the table
below.
To enter the initial conditions, we will set 2 input parameters-temperature and amplitude, as
well as the response time of the model.
Figure 3. Block parameters: Stair generator
The block generates a signal that initially starts at zero. At specified time points (set in the
"Time" parameter: 0, 0.2, 0.4, 0.6 and 0.8 seconds), the signal value changes abruptly according
to the corresponding amplitude (defined in the "Amplitude" parameter). The amplitude varies
between 1000 and 200. The continuous signal is then sampled at a given time step (specified by
the "Sample time" parameter) to generate a sequence of discrete digital values.
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Figure 4. Database collection
3. ANFIS Algorithm
In the simulation of a photovoltaic panel operating at maximum power under varying
environmental conditions (temperature and solar radiation), a fuzzy logic-based controller was
developed. The MATLAB Simulink environment was used to model a solar panel circuit with a
DC-DC converter, where the ANFIS algorithm was applied.
In MATLAB, ANFIS is implemented through the Fuzzy Logic Toolbox, enabling the modeling
of complex nonlinear relationships between inputs and outputs. ANFIS operates as an adaptive
neural network using either Mamdani or Sugeno fuzzy inference systems. Its structure consists
of five layers: input membership functions, normalization, linear combination, and final output
computation. Various software tools, such as MATLAB and Python libraries, provide
implementations of this algorithm, simplifying its practical application.
Figure 5. Simulation of a photovoltaic panel and a DC-DC converter using ANFIS neuro-fuzzy
logic
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Data collection and fuzzy rule formation for anfis system.
The adaptive neuro-phasic inference system (ANFIS) is a combination of artificial neural
networks and phasic logic systems. This system is an effective tool for processing, analyzing
and drawing conclusions about various types of data. This article analyzes the process of data
collection in the ANFIS system, the formation of their fuzzy dependencies and their
management through sigmoid activation mechanisms.
When constructing an anfis model of data collection in ANFIS system, the input parameters are
converted into fuzzy logical variables. The main input parameters in this study include: Stair
generator-phase State of the generator and step performance parameter; Temperature-ambient
temperature; Voltage-output parameter.
The data is formed by connection functions in the form of a sigmoid for the main parameters of
the Stair generator and Temperature. This separation helps to accurately describe the
dependency functions presented in Figures 5 and 6.
Figure 6. MATLAB membership function editor for the Stair generator input variable.
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Figure 7. MATLAB membership function editor for the Temperature input variable.
The advantage of such classification is that the data is more accurately decomposed into phasic
structures and the results are significantly improved.
Affects the Stair generator → Voltage;
Related to Temperature → Voltage.
On the basis of dependencies, sigmoid activation functions are used. The Sigmoid function is
given by:
S x =
1
1 + e
−(ax+b)
Here:
x=Stairgenerator+Temperaturex = Stair generator + Temperature;
a-severity parameter;
b-absorption value.
Fuzzy rule formation (rules) fuzzy logical systems are governed by rules. Since there are two
main input parameters, they are combined and 25 rules are formed:
Figure 8. Anfis Model Structure
1. A dark circle is circled around the input layer, on which the input parameters are indicated
(for example, a stair generator and temperature).
2. Fuzzy Auxiliary Functions (inputmf) – The input signals are transmitted to the fuzzy view
according to the corresponding auxiliary functions.
3. Rule layer - The blue circles show that fuzzy forms 25 rules based on membership functions.
These rules represent the relationship between the two input parameters.
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4. Output of fuzzy membership functions (outputmf) – a result is generated for each rule.
5. Assembly level (output data) – the action of all rules is combined to obtain a single output
result (voltage values).
Table 1. Table of rules for controlling a step generator depending on temperature
St
ai
rg
en
er
at
or
Temperature
VL
L
M
H
VH
VL
VL
L
L
L
M
L
L
L
L
M
M
M
L
L
M
M
H
H
L
M
M
H
H
VH
M
M
H
H
VH
In fuzzy systems, input parameters are interpreted through linguistic variables using fuzzy
dependencies and activation mechanisms such as sigmoid membership functions. For instance,
variables such as the stair-step generator signal and ambient temperature are classified into
linguistic categories: very low, low, medium, high, and very high. This linguistic representation
facilitates adaptive control in the presence of environmental variability.
The use of neuro-fuzzy logic, specifically the Adaptive Neuro-Fuzzy Inference System
(ANFIS), provides significant advantages. It enables the system to dynamically adapt to
fluctuating environmental conditions and varying photovoltaic panel characteristics.
Furthermore, neuro-fuzzy systems exhibit high tolerance to measurement noise and sensor
errors, improving reliability. Their ability to learn from data allows for highly accurate
converter control, and their implementation is supported by mature, readily available software
tools.
Initial system instability observed at the beginning of the simulation—evident through sharp
voltage, current, and power fluctuations—corresponds to the stabilization phase. As the
simulation progresses, these parameters stabilize, reflecting proper functioning of the power
conversion and control architecture. A gradual decrease in the battery’s State of Charge (SOC)
indicates that stored energy is being utilized to supply the load. Despite generally stable voltage
and power delivery to the load, these parameters may still be influenced by network asymmetry,
which can result in additional power losses and reduced efficiency [15], [16].
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Figure 9. Output characteristics of the solar panel, battery and load before applying neuro-fuzzy
logic
In recent developments, fuzzy logic has also been effectively applied to other critical energy
system challenges, including grid monitoring and management. For example, [17] examines
fuzzy logic-based models for improving power distribution networks, emphasizing its
superiority in reactive power compensation and voltage control compared to conventional
methods. Another study [18] presents a methodology using fuzzy logic to evaluate electrical
network performance, considering parameter uncertainties. This approach enhances the
accuracy of analyses related to power quality, supply reliability, and energy loss. The use of
Mamdani-type membership functions and control algorithms enables a more nuanced and
flexible decision-making framework. The integration of neuro-fuzzy logic into the PV system
results in several performance improvements: Enhanced efficiency, due to more precise and
adaptive converter control; Reduced mechanical and thermal stress on components, owing to
smoother and more consistent operation; Greater system robustness against disturbances and
external variability; Improved operational reliability across diverse climatic conditions and
varying solar irradiance levels.
These improvements position neuro-fuzzy control as a promising approach for advanced
photovoltaic system design and broader applications in renewable energy infrastructure.
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Figure 10. Output characteristics of the solar panel, battery, and load after applying neuro-fuzzy
logic
The main distinguishing feature of this research work from others is that the neuro-fuzzy logic
of ANFIS has been added to this model ANFIS. A comparison of the simulated photovoltaic
network with the absence and presence of Fuzzy Logic of the ANFIS element is shown in the
table below.
Tab.2. Characteristics of output parameters before and after ANFIS application
pparametr Without ANFIS
With ANFIS
PV
Voltage
algorithm
(VPV):
Significant fluctuations during the
entire simulation time.
Current
(IPV):
Non-linear
behavior, with clear stepwise
changes, which indicates the
presence of jumps in the load.
Power (PPV): It has more
significant
swings
and
fluctuations.
Voltage (VPV): More stable voltage with minimal
fluctuations.
Current (IPV): Noticeably improved smoothness of
current changes, without sudden jumps.
Power (PPV): Stable behavior with less loss.
Bbattery
Voltage: Almost stable.
Battery Current: Irregular surges
are observed.
Battery Level (SOC): A gradual
decrease characteristic of system
Battery Voltage: Almost perfect stability, which
means less impact on the system.
Battery Current: Reduces surges, which improves
battery life.
Battery Level (SOC): A smoother change that
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operation.
indicates optimized power usage.
Lload
Load
voltage
(Vload):
Fluctuations of relatively high
amplitude.
Load current (Iload): A lack of
stability is observed.
Load power: load fluctuations due
to changes in system operation.
Load voltage (Vload): A significant reduction in
the amplitude of vibrations.
Load current (Iload): Stable behavior, which
indicates an improvement in the quality of system
operation.
Load capacity (Pload): Almost stable power, which
indicates a more efficient transfer of energy to the
load.
The practical implementation of a fuzzy logic-based controller in real-world photovoltaic (PV)
systems involves the integration of several key hardware and software components. These
include microcontrollers or digital signal processors (DSPs) with adequate computational
capabilities to process fuzzy logic algorithms in real time, along with digital voltage and current
sensors to ensure precise measurement of system inputs. Additionally, bidirectional DC-DC
converters with adaptive control logic are essential, supported by development platforms such
as MATLAB/Simulink with the Fuzzy Logic Toolbox or embedded libraries tailored for real-
time controller deployment.
While the Adaptive Neuro-Fuzzy Inference System (ANFIS) offers significant benefits—such
as improved adaptability to variable solar irradiance and environmental conditions—it also
presents several practical challenges. These include: High computational complexity,
necessitating robust processing hardware; Model training requirements, which demand
historical performance data to accurately calibrate the system; Latency in decision-making,
potentially longer than that of heuristic-based algorithms; Increased system cost, driven by the
need for high-performance controllers and precision sensors.
Despite these challenges, ANFIS remains a promising solution for enhancing the efficiency and
intelligence of PV energy systems. To objectively evaluate its effectiveness, it is useful to
perform a comparative analysis of ANFIS against other Maximum Power Point Tracking
(MPPT) methods—such as Perturb & Observe (P&O), Incremental Conductance (IncCond),
and Artificial Intelligence (AI)-based algorithms—based on key performance metrics like
tracking accuracy, response time, and computational load.
Below is a conceptual comparison framework:
MPPT Method
Accuracy
Response Time Computational Complexity
ANFIS
High
Moderate
High
Perturb & Observe
Low to Moderate Fast
Low
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MPPT Method
Accuracy
Response Time Computational Complexity
Incremental Conduction
Moderate to High Moderate
Moderate
AI-Based (e.g., ANN, DNN) Very High
Fast to Moderate Very High
This table provides a high-level overview of the trade-offs involved in selecting an appropriate
MPPT strategy. While ANFIS provides superior accuracy and adaptability, especially under
dynamic conditions, its complexity may be prohibitive for systems with limited processing
resources or cost constraints.
Table 3. Comparison of MPPT methods
MPPT Method
Accuracy
Response Time Computational Complexity
Shading
Tolerance
Perturb & Observe
(P&O)
Medium
Fast
Low
Poor Adaptation
Incremental
Conductance
(IncCond)
High
Medium
Medium
Tolerates
Slow
Changes
AI (NN, GA, etc.) Very High
Medium
High
Good Adaptation
ANFIS
Very High
Medium
High
Excellent
Adaptation
This table is a summary of information from a number of scientific studies listed in [19]-[25].
Despite the computational complexity and the need for advanced training, ANFIS demonstrates
excellent adaptability to changing lighting conditions, which is especially important for
photovoltaic systems operating in unstable environments.
A study published [26] provides the following indicators for various MPPT methods with
illumination of 1000 W/m2 and a maximum power of 250 W:
P&O: Average power of 237.4 W, efficiency of 94.96%, convergence time of 0.004 s.
IncCond: Average power of 239.1 W, efficiency of 95.60%, convergence time of 0.006 s.
ANFIS: Average power 244.4 W, efficiency 97.76%, convergence time 0.046 s.
Neural networks: Average power 244.6 W, efficiency 97.84%, convergence time 0.205 s.
Hybrid method: Average power 247 W, efficiency 98.80%, convergence time 0.2005 s.
Comparing the presented MPPT methods, it can be noted that ANFIS demonstrates high
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efficiency (97.76%) and close to maximum output power (244.4 W), surpassing the classical
P&O and IncCond methods. Although its convergence time (0.046 s) is longer than that of
these methods, it is significantly lower than that of neural networks and the hybrid method. This
makes ANFIS the optimal compromise between efficiency and speed, providing more accurate
tracking of maximum power with an acceptable response time.
Conclusion:
This study presents the modeling of a photovoltaic (PV) system integrated with a bidirectional
DC-DC converter in the MATLAB/Simulink environment, with a primary focus on
implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for intelligent control. The
simulation results confirm the high accuracy and effectiveness of the proposed approach across
a wide range of operating conditions.
A comparative analysis with traditional Maximum Power Point Tracking (MPPT) methods—
including Perturb & Observe (P&O), Incremental Conductance (IncCond), and artificial
intelligence (AI)-based techniques—demonstrates that ANFIS offers superior adaptability to
variations in solar irradiance and ambient temperature. This capability is particularly valuable
for PV systems deployed in regions with unstable or unpredictable climatic conditions.
Despite its increased computational requirements and the necessity of preliminary training on
historical data, ANFIS provides several critical advantages over conventional control methods:
Enhanced System Stability: ANFIS significantly reduces fluctuations in voltage, current, and
power on both the generation and load sides. It ensures consistent system behavior even under
rapidly changing environmental conditions.
Improved Energy Efficiency: The algorithm enables adaptive management of power flows
between the solar panel, battery, and load, resulting in more effective utilization of the available
energy. Enhanced MPPT accuracy minimizes energy losses and boosts overall system
performance.
Higher Power Quality: Systems controlled by ANFIS exhibit lower harmonic distortion and
reduced signal ripple, contributing to improved quality of the delivered electrical energy.
The results strongly indicate that ANFIS is a viable and promising solution for advanced PV
systems requiring high levels of precision, adaptability, and operational stability. Future
research should explore strategies to reduce the computational burden of ANFIS and develop
embedded hardware solutions capable of supporting its real-time implementation in commercial
solar energy systems.
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