ISSN: 2319-8753

International Journal of Innovative Research in Science,

Engineering and Technology
(An ISO 3297: 2007 Certified Organization)

Vol. 3, Issue 7, July 2014

Design and Development of Intelligent MPPT

Controller for Optimal Tracking Photovoltaic
System
Prashant Upadhyay1, Bheru Das Vairagi2, Dr. Vinod Kumar3, Dr. R.R. Joshi4
P.G. student, Dept. of Electrical Engineering, College of Technology and Engineering, Udaipur, Rajasthan, India 1,2
Asst. Professor, Dept. of Electrical Engineering, College of Technology and Engineering, Udaipur, Rajasthan, India 3
Professor, Dept. of Electrical Engineering, College of Technology and Engineering, Udaipur, Rajasthan, India 4
ABSTRACT: Maximum power point trackers are so important to improve the efficiency of photovoltaic systems.
Many methods have been proposed to achieve the maximum power that the PV modules are capable of producing
under different atmospheric conditions. This paper proposed a fuzzy logic based Maximum Power Point Tracking
(MPPT) algorithm for solar system. The solar panel is simulated and analyzed in MATLAB/SIMULINK. Photovoltaic
system is connected to a DC-DC Buck-boost converter. The Solar panel can produce maximum power at a certain
operating point called Maximum Power Point (MPP).To achieve maximum power and to get maximum efficiency, the
whole system must operate at that Maximum Power point. Maximum power point of PV panel keeps same on changing
with changing solar irradiance and temperature of cell. Then to obtain maximum power from a PV system, MPPT
algorithms are implemented. So that, fuzzy logic based MPPT is developed and Simulation results show the effective
of the fuzzy based controller to produce more stable power.
KEYWORDS: MPPT, Fuzzy Logic, PV Modeling, Buck-Boost Converter.
I. INTRODUCTION
As we know energy has the great role in our life and economy. The energy demand has greatly increased day by
day due to the technical revolution. Fossil fuels have been started to be gradually depleted. On the other hand the
greenhouse gases emissions are increasing due to the conventional generation of energy. It is a really critical challenge
to reduce carbon dioxide emissions and ensuring safe, improve and affordable energy, and to achieve better energy
systems. Renewable energy sources are considered for generation of clean and sustainable energy.[1]
There are many available renewable energy sources are such as solar energy, wind energy, etc. Photovoltaic
(PV) system has a huge research area for upcoming energy requirement. So it taken a great attention by the researchers
and it appears to be one of the most sustainable renewable energy sources. Solar energy is a secure, less maintenance,
pollution free and noiseless generation due to absence of moving parts.[2,3]
However, two important factors affect the implementation of photovoltaic systems. These are high initial cost
and less efficient at energy conversion. So to reduce photovoltaic system cost and to increases the conversion
efficiency of solar energy, the maximum power point tracking system of photovoltaic modules is one of the effective
method. Maximum power point tracking, frequently referred to as MPPT, is a system used to extract the maximum
power of the PV module to deliver it to the load, and system efficiency increases.

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ISSN: 2319-8753
International Journal of Innovative Research in Science,
Engineering and Technology
(An ISO 3297: 2007 Certified Organization)

Vol. 3, Issue 7, July 2014

PV systems are dependent power sources with nonlinear I-V characteristics under different environmental
(insolation and temperature) conditions. In addition they have high installation cost and low energy conversion
efficiency. These are the reasons for the less efficiency of PV systems. To overcome the problems, the Maximum
power point tracking of the PV system is used (at a given condition) at on-line or off-line algorithms and the system
operating point is forced toward this to desired condition.[4,5]
In literature, various maximum power point tracking (MPPT) techniques are proposed and implemented. These
techniques include look-up table methods, perturbation and observation (P&O) methods and computational methods.
One of the computational methods which have demonstrated fine performance using fuzzy-based MPPT technique.[6]
The fuzzy theory based on fuzzy logic sets and fuzzy algorithms provides a general method of expressing
linguistic rules so that they may be processed quickly by a computer. Recently some application of fuzzy control has
been successful in photovoltaic applications. The fuzzy controller introduced in uses dP/dI and its variations D(dP/dI)
as the inputs and computes MPPT converter duty cycle.[7] The shortcoming of this approach is the ignorance of duty
cycle variations, which results in an acceptable accuracy level with improved dynamic characteristics. The fuzzy
tracker of reference considers variation of duty cycle, but replaces dP/dI by the variation of panel power. This tracker
has fine dynamic behavior with limited accuracy.[8]
This paper presents a photovoltaic system including a solar panel, a fuzzy MPP tracker and a resistive load is
designed, simulated and constructed. Simulated and measured results are presented.
II. RELATED WORK
A. THE PHOTOVOLTAIC SYSTEM
There are many equivalent circuits of a solar cell, where the single-diode and two-diode models could be the
mostly used. So that the single-diode model is simple and accurate enough in many cases. Its equivalent circuit with
series and parallel resistance is shown in Figure 1.[3,4]

Fig. 1: Equivalent circuit of a single solar cell

The symbols in Fig. 1 are defined as follows:

Iph
: photocurrent;
Id
: current of parallel diode;
Ish
: shunt current;
I
: output current;
V
: output voltage;
D
: parallel diode;
Rsh : shunt resistance;
Rs
: series resistance;
Using the equivalent circuit of Fig.1, the nonlinear I-V characteristics of solar cells is given by the following
equation:
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ISSN: 2319-8753
International Journal of Innovative Research in Science,
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(An ISO 3297: 2007 Certified Organization)

Vol. 3, Issue 7, July 2014

q ( vR s I )
V R sI
I I ph I 0 e AKT 1
R sh

.(1)
Where Io is the reverse saturation current of the diode, q is the electron charge (1.602 1019 C), A is the
curve fitting factor, and K is Boltzmann constant (1.38 1023J/K).

Model of a PV Module
If a solar cell type tends to have an IV curve in which the slope at short circuit is almost zero, the value of Rsh
can be assumed to be infinite. In this case, the last term in (1) could be ignored. And taking Iph as ISC, (1) will become

q ( v R sI )
I ISC I 0 e AKT 1
.(2)

Where ISC is the short-circuit current. Equation (2) is valid for a solar cell. For the accurate application of this
equation for a PV module, the term of q(V + Rs I)/AKT is changed to q(V + Rs I)/NsAKT, in which Ns is the number
of series-connected solar cells in a PV module.
A simple PV module model will be derived from (2) in this section. When a PV module is in a open-circuit
situation, I = 0 and the item q/NsAKT in (2) will be solved as follows:
q

N s AKT

ISC
In

I 1
0

VOC
.(3)

where VOC is the open-circuit voltage of a PV module. Substituting (3) into (2), we get
I SC
(v R s I )

I In ( 1) VOC
1
I ISC 1 o e I o

ISC

.(4)
Fig. 2 shows the P-V curves of the PV module under changing solar radiation from 200W/m2 to 1000W/m2
while keeping the temperature constant at 25C. Fig. 3 shows the P-V curves of the PV module under changing
temperature from 10C to 55C while keeping the solar radiation constant at 1000W/m2.

Fig. 2- P-V curves under changing the solar radiations

Fig. 3- P-V curves under changing the Temperature

B. DC-DC BUCK-BOOST CONVERTER

The DC-DC converter is an electronics circuit which is used to provide a loss less transfer of energy between
different circuits at different DC voltage levels. There are many DC-DC converters. One of the popular types of DCDC converters is buck-boost converter. Buck-boost converter is circuit that operating using switching mode power
supply. Buck-boost converter used to step up and step down the DC voltage by changing the duty ratio of the switch.
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ISSN: 2319-8753
International Journal of Innovative Research in Science,
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(An ISO 3297: 2007 Certified Organization)

Vol. 3, Issue 7, July 2014

When the duty ration is less than 0.5, the output voltage is less than the input voltage and vice versa. The buck-boost
converter circuit is shown in Fig. 4.[9]

Fig. 4: Schematic of a buckboost converter.

The operation of the Buck-boost converter is as follows:

When the transistor is turned ON, the diode is reverse-biased and being not conducting. Turning on the
transistor is accomplished during o < t < DT s interval. The voltage across the inductor in this stage is given
as:
.(5)
When the transistor is turned off, the diode is conducting. Turning on the transistor is accomplished during
DTs< t < Ts interval. The voltage across the inductor in this stage is given as:
VL VO .(6)
It is known that for steady-state operation, the net change in the inductor current must be zero over one
switching cycle. By applying volt-second balance we get:
VI D + VO (1 - D) = 0 .(7)
Where D is the duty cycle of the converter which is given as:
T
D on
T .(8)
From (7) the relation between the input voltage to the output voltage of the buck-boost converter is given as[3]:
Vo D

Vi
D 1) .(9)
III. THE PROPOSED FUZZY LOGIC BASED MPPT METHOD
A Fuzzy Logic Controller (FLC) is designed to work as an MPPT controller. Fig. 5 shows the FLC.

Fig. 5: Fuzzy logic controller

The FLC contains a Fuzzy Inference System (FIS) whose structure is shown in Fig. 6. The FIS inputs, error (E) and
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ISSN: 2319-8753
International Journal of Innovative Research in Science,
Engineering and Technology
(An ISO 3297: 2007 Certified Organization)

Vol. 3, Issue 7, July 2014

change in error (CE), are obtained using the following equations:[10]

E(k )

PPV (k ) PPV (k 1)
I PV (k ) I PV (k 1) .(10)

CE(k) E(k) E(k 1)

Where:
PPV:
IPV:
E:
CE:

.(11)

The power of the PV system

The current of the PV system
Error
Change in error
Fuzzification

Inference

Rule Base
Relationship
Defuzzification
Fig. 6: Fuzzy Interface System

A. Fuzzification:
1)

2)

3)

Fuzzification of Error-Signal: The range of error-signal is partitioned into seven regions with triangular and trapezoidal
membership functions labeled as: Negative Big, Negative medium, Negative small, Zero error, positive small, positive
Medium and positive big over Universe of Discourse (UoD) of -2 to 2 as shown in fig.7.
Fuzzification of Change in Error-Signal: The second input parameter is Change in Error-Signal. The range of signal is
partitioned into seven regions with triangular membership functions labeled as: Negative Big, Negative medium, Negative
small, Zero error, positive small, positive Medium and positive big over UoD of -3.5 to 3.5 same as shown as error signal.
Fuzzification of PWM Duty cycle-Signal: Defuzzification converts membership functions into Crisp value for PWM
signal. Seven regions with triangular and trapezoidal membership partitions are labeled as: Negative Big, Negative
medium, Negative small, Zero error, positive small, positive Medium and positive big over UoD of 0.1 to 0.6 counts.

(a)

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ISSN: 2319-8753
International Journal of Innovative Research in Science,
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(An ISO 3297: 2007 Certified Organization)

Vol. 3, Issue 7, July 2014

(c)
Fig.7 Graphical view of the membership function (a) error signal; (b) Change of Error Signal; (c) Duty Cycle.

B. Fuzzy Inference System:

The Fuzzy Inference forms a key part of Fuzzy Logic Control. The Fuzzy IF-THEN rule base matrix is in[11]
Table 1: Rule Based Matrix

E/CE
NB
NM
NS
ZE
PS
PM
PB

NB
ZE
ZE
NS
NM
PS
PM
PB

NM
ZE
ZE
ZE
NS
PM
PM
PB

NS
ZE
ZE
ZE
ZE
PM
PM
PB

ZE
NB
NS
ZE
ZE
PS
ZE
ZE

PS
NB
NM
NS
ZE
ZE
ZE
ZE

PM
NB
NM
NS
PS
ZE
ZE
ZE

PB
NM
NM
NS
PM
ZE
ZE
ZE

C. Defuzzification:
The Fuzzy Inference based on Mamdanis scheme is shown in fig.8 for present Error of 0.33, change of error
is -0.795 thereby suggesting a PWM duty cycle of 0.525.[10,11]

Fig.8: Rule Viewer

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International Journal of Innovative Research in Science,
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Vol. 3, Issue 7, July 2014

IV. RESULT AND DISCUSSION

In order to verify the MPP tracker for the photovoltaic simulation system, the proposed fuzzy MPPT method
can effectively and accurately tracks the maximum power. The simulation is done using MATLAB/STMULINK. The
output of the fuzzy MPPT control block is the gating signal which is used to drive the MOSFET. With further tuning of
fuzzy sets and fuzzy rules the process of output power can be more and more power efficient. Results are shown in fig.
9.

Fig. 9: Performance of Fuzzy logic controller

V. CONCLUSION
Fuzzy MPPT model using Matlab/SIMULINK and design of appropriate DC-DC buck-boost converter with a
maximum power point tracking facility are presented in this paper. A new method for MPPT based fuzzy logic
controller is presented. The model is tested under 1000W/m2 solar radiation and 25C photovoltaic temperature.
Simulation results show that the proposed method effectively tracks the maximum power point as compared to other
MPPT techniques. The oscillation around MPP is decreased and the response is faster in compared with the
conventional methods.
REFERENCES
[1]
[2]

S. Chowdhury and S. P. Chowdhury, Mathematical modelling and performance evaluation of a stand-alone polycrystalline PV plant with
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Zheng Zhao, Ming Xu, Qiaoliang Chen, Jih-Sheng Lai and Younghoon Cho, Derivation, Analysis, and Implementation of a BoostBuck
Converter-Based High-Efficiency PV Inverter, IEEE Transactions on Power Electronics, vol. 27, No. 3, pp. 1304-1313, March 2012.

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International Journal of Innovative Research in Science,
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Vol. 3, Issue 7, July 2014

E. Babaei, Esmaeel Sayed and H.M. Marhery, Operational Modes and Output-Voltage-Ripple Analysis and Design Considerations of Buck
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[9] C.S. Chin, P. Neelakantan, H.P. Yoong and K.T.K. Teo, Fuzzy logic MPPT for photovoltaic modules influenced by solar irradiation and cell
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[3]

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