Analysis and Implementation of Fuzzy Logic Controller Based MPPT to Enhance Power Quality in PV System

International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 6 Issue 5, July-August 2022 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD50541 | Volume – 6 | Issue – 5 | July-August 2022 Page 724
Analysis and Implementation of Fuzzy Logic Controller
Based MPPT to Enhance Power Quality in PV System
Akash Kumar Gupta1
, Manish Prajapati2
1
Student, 2
Assistant Professor,
1,2
Bhabha Engineering Research Institute, Bhopal, Madhya Pradesh, India
ABSTRACT
Maximum power point trackers are so important in photovoltaic
systems to increase their efficiency. Many methods have been
proposed to achieve the maximum power that the PV modules are
capable of producing under different weather conditions. The output
power induced in the photovoltaic modules depends on solar
radiation and temperature of the solar cells. Therefore, to maximize
the efficiency of the renewable energy system, it is necessary to track
the maximum power point of the PV array. This paper proposes a
new method of maximum power point tracking using fuzzy logic for
photovoltaic system. It uses a sampling measure of the PV array
power and voltage then determines an optimal increment required to
have the optimal operating voltage which permits maximum power
tracking. This method carries high accuracy around the optimum
point when compared to the conventional perturbation and
observation algorithm then it assures fast and fine tracking. The
system is composed a solar panel, a Boost converter and Fuzzy Logic
Controller (FIS). The simulation results show that the proposed
maximum power tracker could track the maximum power accurately
and successfully in all condition tested.
KEYWORDS: Solar (PV), Boost Converter, Fuzzy Logic Controller
(FIS), MPPT, MATLAB Simulink
How to cite this paper: Akash Kumar
Gupta | Manish Prajapati "Analysis and
Implementation of Fuzzy Logic
Controller Based MPPT to Enhance
Power Quality in PV
System" Published
in International
Journal of Trend in
Scientific Research
and Development
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Issue-5, August 2022, pp.724-733, URL:
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1. INTRODUCTION
Energy has become an important and one of the basic
structures for economic development of a country[1].
It plays a vital role in the day-to-day lives of the
human beings, in the socio-economic development
and welfare of a country. Consumption of a large
amount of energy in a country indicates increased
activities in the domestic, agricultural, transportation
and industrial sectors. All this amounts of a better
quality of life. Therefore, the per capita energy
consumption of a country is an index of the standard
of living or prosperity of the people of the country.
The gap between supply and demand is continuously
increasing. To bridge up this gap, researchers and
scientists tried to find out an alternate solution and
solar energy has been found to be the best alternative
for this problem [1], [2].
Energy sources are divided into two groups namely
conventional or non-renewable and non-conventional
or renewable energy sources. At present conventional
sources are used as the primary source for generation
of electricity worldwide. The conventional energy
sources are derived from ground in the form of solid,
liquids and gases. Coal is a solid, Crude oil is the only
natural liquid and Natural gas and propane are the
gases that are available worldwide. Coal, petroleum,
natural gas, and propane are all considered as fossil
fuels because they are formed from the buried remains
of plants and animals that lived millions of years ago.
Uranium ore, a solid, is mined and converted to a fuel.
These energysources are considered as non-renewable
because they cannot be replenished in a short period
of time. Moreover, excessive use of the fossil fuels,
radioactive material, emission of carbon dioxide
(CO2), methane (CH4) and other greenhouse gases
are some of the main threats for the global climate
system and our natural living conditions [3].
2. SOLAR ENERGY is the major source of energy
that helps to sustains life on the earth for all plants,
animals and human beings. The earth receives this
radiant energy from the sun uniformly in all direction
IJTSRD50541

International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
in the form of electromagnetic waves. The output of
the sun is 2.8 × 1023 kW/year. The energy reaching
the earth is 1.5 × 1018 kWh/year. The Sun acts as a
perfect emitter of radiation (black body) at a
temperature close to 58000K. Solar radiation emitted
from the sun covers a continuous spectrum of
electromagnetic radiation in a wide range of
frequency. Around 99% of the extra- terrestrial
radiation has wavelengths in the range from 0.2 to
4µm with peaking at
0.48 µm [1]. The spectrum of the solar radiation at the
top of the atmosphere is shown in Fig. 1.1. The
intensity of the solar radiation keeps on attenuating as
it propagates away from the surface of the sun,
although the wavelengths remain unchanged. Solar
radiation that falls on the outer surface of the earth is
known as Extra-terrestrial Radiation. The solar
radiation that reaches the earth surface after passing
through the earth’s atmosphere is known as Terrestrial
Radiation. The energy received from the sun per unit
time, on a unit area of surface perpendicular to the
direction of propagation of the radiation at the top of
the atmosphere and at the earth’s mean distance from
the sun is defined as the Solar Constant. The World
Radiation Centre (WRC) has adopted the value of the
solar constant as 1367 W/m2. The measure of power
density of sunlight received at a location on the earth
is defined as the Irradiance and is measured in W/m2.
Irradiation is the measure of energydensityof sunlight
and is measured in kWh/m2. Irradiance and irradiation
apply to all components of the solar radiation.
When the solar radiation enters the earth’s
atmosphere, a part of the incident energy is lost by
absorption by air molecules, clouds and particulate
matter usually referred as aerosols and a part by
reflection or scattering by dust particles. Solar
radiation that is not reflected or scattered and received
at the earth surface without change of direction, i.e., in
line with the sun is called Beam or Direct Radiation.
Solar radiation scattered by aerosols, dust and
molecules is known as Diffused Radiation. It does not
have a unique direction. Some of the radiation
received after reflection from the ground, and is called
Albedo. The total radiation consisting of these
components is called Global Radiation. The global
irradiance can be as high as 1 KW/m2, but the
available irradiance is usually less than this maximum
value because of the rotation of the earth and adverse
weather conditions [1], [2].
3. METHODOLOGY: MAXIMUM POWER
POINT TRACKING Most extreme Power Point
Tracking (MPPT) is helpful apparatus in PV
application. Sun oriented radiation and temperature
are the primary factor for which the electric power
provided by a photovoltaic framework. The voltage at
which PV module can create greatest power is called
'most extreme power point (pinnacle control voltage).
The primary rule of MPPT is in charge of separating
the greatest conceivable power from the photovoltaic
and feed it to the heap by means of dc-to-dc converter
which steps up/ down the voltage to required size [5].
There are many maximum power point techniques.
Among them, two MPPT techniques of ANN, perturb
and observe (P&O) have been selected for the purpose
of comparison in this paper.
A. DC/DC Boost Converter The dc-dc converter is
used to supply a regulated dc output with the given dc
input. These are widely used as an interface between
the photovoltaic panel and the load in photovoltaic
generating systems. The load must be adjusted to
match the current and voltage of the solar panel so as
to deliver maximum power. The dc/dc converters are
described as power electronic switching circuits. It
converts one form of voltage to other. These may be
applicable for conversion of different voltage levels.
Fig. 1 shows the circuit diagram of dc-dc boost
convertor [7].
Fig. 1: Circuit Diagram of Boost Converter
The dc-dc boost converter circuit consists of Inductor
(L), Diode (D), Capacitor (C), load resistor (RL), the
control switch(S). These components are connected in
such a way with the input voltage source (Vin) so as
to step up the voltage. The output voltage of the boost
converter is controlled by the duty cycle of the
switch. Hence by varying the ON time of the switch,
the output voltage can be varied. The relationships of
input voltage, output voltage and duty cycle are as
follow:
(1)
Where, Vin, Vo are the input and output voltage of the
converter and D is the duty cycle of the control
switch.
3.1. Fuzzy Logic Controller Design and System
Simulation
This chapter studies the application of fuzzy logic
control as algorithm for a maximum power point
tracking of photovoltaic system. Also the design and
performance of the fuzzy system was tested in
Simulink/MATLAB with PV module and buck-boost

converter and the rules of fuzzy logic were
formulated and validated. Finally, the results in this
section were useful to be implemented in the real
fuzzy control system on actual hardware which is
presented in next chapter.
3.1.1. PV Module with MPPT based Fuzzy Logic
Controller
Figure 2 illustrates the basic diagram of a fuzzy logic
based maximum power point tracker. It can be seen
from the observation that the only two state variables
are sensed as inputs of fuzzy controller; output
voltage and output current of PV module (Vm) and
(Im). The fuzzy logic controller, from measurements,
gives a signal proportional to the converter duty cycle
(D) which is fed to the converter through a pulse
width modulator. This modulator drives the value of
D to perform Pulse Width Modulation (PWM) that
generates the control signals for the converter switch
(s). The fuzzy logic controller scheme is defined a
closed loop system.
Figure 2: Fuzzy control scheme for a maximum power point tracker.
3.1.2. Fuzzy controller structure
Fuzzy logic controller structure is based on fuzzy sets where a variable is a member of one or more sets with a
specified degree of membership. Benefits of using Fuzzy logic are; it allows us to emulate the human reasoning
process in computers, quantify imprecise information, make decision based on vague information such as
resistive load is connected to the PV module through the buck boost dc-dc converter. The block diagram of
MPPT based fuzzy logic control is shown in Figure 3.
Figure 3: Block diagram of the fuzzy logic algorithm.
Also, Figure 4 illustrates the basic architecture of fuzzy controller which contains of the three following blocks:
3.1.2.1. Fuzzification
Each input/output variable in the fuzzy logic controller is required which define the control surface that can be
expressed in fuzzy set notations using linguistic levels. Each input and output variables of linguistic values
divide its universe of discourse into adjacent intervals to form the membership functions. The member value
represents the extent at which a variable belongs to a particular level. The converting of input/output variable
process to linguistic levels is named as Fuzzification.
3.1.2.2. Inference
A set of rules are used to governor the behavior of the control surface which relates the input and output
variables of the system. A typical rule would be:
If x is A THEN y is B.

Each of the rules that has any degree of truth in its premise, when a set of input variables are read, a rule is fired
and contributes to the forming of the control surface by approximately modifying it. Then, when all the rules are
fired that resulting control surface. It is expressed as a fuzzy set to represent the constraints output. This process
is called inference.
3.1.2.3. Defuzzification
Defuzzification is known as the process of conversion of fuzzy quantity into crisp quantity. There are many
methods available for Defuzzification. The most common one is centroid method, which
illustrated by the following formula:
(2)
Where is the membership degree of output x.
Figure 4: Structure of fuzzy logic controller.
3.2. Membership functions of the proposed fuzzy system
Fuzzy sets for each input and output variable are defined as shown in Figure 5. Three fuzzy subsets; small,
medium, and high were chosen for the inputs and output variables of fuzzy controller. As shown in Figure 5,
trapezoidal shapes have been adopted for the membership functions. The range of the inputs memberships which
are PV voltage and PV current were modified according to the characteristics of proposed PV module (Voc 22
V, Isc = 15 A) and buck-boost converter that were presented in chapter two and chapter three respectively. Also,
the duty cycle which represents the output of fuzzy controller was ranged between zero and one to give more
flexibility for switching the buck-boost converter.
(a)
(b)

(c)
Figure 5: Membership functions for (a) input current of converter (b) input voltage of converter (c)
output duty cycle of fuzzy controller.
3.2.1. Derivation of Control Rules
Fuzzy control rules are extracted by analyzing the system behavior. The different operating conditions are
considered in order to improve tracking performance in terms of dynamic response and robustness. The
algorithm can be explained as follows:
The tracking process is started with an initial duty cycle, D= 0. The converter input current Im, and voltage Vm,
are then measured and sense the duty cycle that can give maximum power output of the converter at that time
based on predicted values that have already been entered into fuzzy
system. This operation repeats itself continuously until the power reaches the maximum value and the system
becomes stable.
3.2.2 Tuning of Control Rules
The fuzzy rules of the proposed system have been derived from the system behavior and tested in
Simulink/MATLAB. Table 1 indicates the rules based on the membership functions that shown in Figure 5.
Table 1: Fuzzy controller rules
Current/Voltage small medium high
small H H M
medium H H M
high M M M
The rules in the table above are read as:
If (current is small) AND (voltage is small) then (MD is high)
If (current is small) AND (voltage is medium) then (MD is high)
If (current is small) AND (voltage is high) then (MD is medium)
If (current is medium) AND (voltage is small) then (MD is high)
If (current is medium) AND (voltage is medium) then (MD is high)
If (current is medium) AND (voltage is high) then (MD is medium)
If (current is high) AND (voltage is small) then (MD is medium)
If (current is high) AND (voltage is medium) then (MD is medium)
If (current is high) AND (voltage is high) then (MD is medium)
Where MD is duty cycle that represents the output of fuzzy controller.
The fuzzy logic algorithm was simulated in Simulink/MATLAB using the fuzzy logic toolbox and the rules
were tuned precisely. The basic window of fuzzy designer is illustrated in Figure 6 where the controller based on
Mamdani's fuzzy inference method and centroid method as a defuzzification process is used.

Figure 6: Fuzzy logic designer in Matlab tool box.
Figure 7 demonstrates the fuzzy controller rule surface which is a graphical representation of the rule base. And
Figure 8 shows the rule viewer which indicates the operation of the fuzzy controller during the change of inputs.
Figure 7: Graphical representation of fuzzy controller rules.
Figure 8: Fuzzy controller rule viewer.

After the fuzzy controller was modified in MATLAB the FIS file was created in order to be called in the
Simulink system.
SIMULATION AND RESULTS
The entire system was combined and tested in Simulink/MATLAB for values of solar irradiation and
temperature as shown in Figure 4.1.
Fig 4.1 MATLAB/ Simulink ANN MPPT Algorithm for Solar PV System
Fig 4.2 Fuzzy Logic MPPT Block
The simulation model shown in Figure 4.1 was implemented in at Simulink/MATLAB. In order to check the
fuzzy controller performance and the efficiency of the converter the readings of input power and output power of
the MPPT were taken at solar irradiance (1000w/m^2) & temperature and also duty cycle was observed at
the same values.
To analyse the performance of the output voltage, the output currents and the output power are measured as
shown. The PV parameters of the system are shown in Table 4.1.
TABLE 4.1 PV PARAMETERS
Parameters Specifications
Maximum power, Pm 230.4 W
Series-connected modules per string 1 nos
Parallel strings 1 nos
Cells per module (Ncell) 60
Voltage at maximum power point Vmp (V) 30.72 V
Current at maximum power point Imp (A) 7.5 A
Open circuit voltage, Voc 37.14V
Short circuit current, Ise 8 A

The average efficiency of the converter was about 98.49% which means the MPPT based fuzzycontroller obtains
the maximum power that can be extracted from the PV module at the specification of the proposed system.
Figures from 4.3 to 4.7 illustrate the Maximum Power Output of the MPPT based fuzzy controller and boost
converter versus pulse width modulation (PWM) at each input and output power.
Fig 4.3
Fig 4.4
Fig 4.5

Fig 4.6
Fig 4.7
A Fuzzy controller for tracking maximum power point of photovoltaic source was proposed in this chapter and
simulated in Simulink/MATLAB. The controller was based on the basic blocks of fuzzy system, which are
(Fuzzification, Inference, and Defuzzification). These blocks read inputs of fuzzy and program the process of the
plant and convert the program into output action respectively. The trapezoidal shapes of inputs and output
membership functions were proposed in this controller and Mamdani's fuzzy inference method and centroid
method as a Defuzzification process were chosen for this controller as well. The whole system includes PV, boost
converter, fuzzy controller, and load was modeled and simulated under fixed irradiance and temperature. The
results indicate that the proposed fuzzy controller performed well and valid to be implemented on real time
system.
5. CONCLUSION & FUTURE SCOPE
5.1. CONCLUSION
This thesis has presented a new fuzzy logic controller
(FLC) for controlling MPPT of a stand-alone
photovoltaic system. The FLC adjust appropriately
the optimal increment's magnitude voltage required
for reached the operating voltage and tracking the
MPP, whatever solar radiance and temperature. The
proposed controller avoids the oscillation problem of
the perturbation and observation algorithm in MPPT
method to assure fast and fine tracking. The FLC was
simulated. The simulation results show the robustness
of the FLC for variation in environmental conditions,
the PV system is operating at the maximum power
point.
The proposed fuzzy logic controller speed-up the
procedure of reaching the accurate maximum power
point of a photovoltaic array, it is suitable for any
DC/DC topology and gives robust performances
under variations in environmental operating
conditions and load.

5.2. FUTURE SCOPE
The possible extensions of the presented work are
listed as:
In this thesis, Mamdani method was used to
implement the fuzzy inference system; in future
work it may be replaces by Sugeno approach and
compare it with Mamdani approach.
Optimization of membership functions and the
rule base of FLC using GA can be done to further
improve the efficiency of the SPV system.
Other optimization techniques such as PSO, DE
or other hybrid technique like neuro-fuzzy maybe
used for the optimization of the proposed FLC.
The performance of the presented SPV system
can be determined for the different types of loads
like battery or motors instead of resistive load.
Experimentation can be performed for the SPV
system having inbuilt bypass diodes to depicts its
performance under the partial shading condition.
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