This document presents the seminar details for a project on maximum power point tracking (MPPT) for a photovoltaic system. The project aims to compare the Buck-boost, Cuk, Sepic and Zeta DC-DC converters for use in an MPPT system using an incremental conduction MPPT algorithm. The methodology will involve simulating the four converters in MATLAB, observing their output characteristics, and selecting the best converter. A literature review covers previous work on MPPT algorithms and converter selection. The project schedule outlines tasks over 10 months including simulation implementation, comparisons of results, and final submission.

1. SEMINAR ON
Project Name AAAAAAAAAAAAAAAAAAA
system
Submitted by:
AAAA
Guided by:
BBBBB
Department of Electronics And Telecommunication
Government College of Engineering , Yavatmal
Yavatmal – 445001
Dr. Babasaheb Ambedkar Technological University, Lonere
2022-23

2. CONTENTS
 Abstract
Introduction
 Literature Survey
 Problem Definition
 Proposed Methodology
 Project Schedule
 Proposed project report contents
 References

3. ABSTRACT
Photovoltaic electricity generation is one alternative to use of
renewable energy source. Efficiency of photovoltaic systems can be
upgraded by operating photovoltaic panel at its optimum power point
by using maximum power point tracking. Getting maximum power
from the solar panels needed DC-DC converters by using the algorithm
of MPPT. This method presents a comparison study of the Buck-boost,
Cuk, Sepic and Zeta converters as a device of MPPT. The algorithm for
maximum power point tracking will be a incremental conduction
method.
The simulation results of fourth converters were evaluated and
compared, the parameters were observed of each DC-DC converters
such forms of ripple current signal, the voltage on the input and the
output sides, the input power and output power, as well as a steady state
period and isolation signal between the input and output sides. Finally
best converter with incremental conduction method will be suggest for
design solar pv system.

4. INTRODUCTION
 Advantages of solar pv system
 Limitation of solar pv system
 Low power efficiency
 Function of MPPT
 Types of MPPT
 Need of DC-DC Converter
 Failure of DC-DC Converter
 Proper selection of converter

5. Literature Survey
Sr no Name of authors
and year
Title of paper Methodology Claim by author
1. Youcef Soufi,
Mohcene Bechouat,
Sami Kahla
(2016)
Fuzzy-PSO controller
design for maximum
power point tracking in
photovoltaic system
In this paper, the application of
this approach based MPPT
algorithm for Photovoltaic power
generation system operating under
variable conditions is proposed to
optimize and to design an
intelligent controller comparing to
conventional one.
It has a faster tracking speed, it
exhibits zero oscillations at the
MPP, it could locate the MPP for any
environmental variations
and large fluctuations of insulation
and this algorithm can be easily used
and can be computed very rapidly
2. Chao Huang, Long
Wang, Ryan Shun-
cheung Yeung, Zijun
Zhang, Henry Shu-
hung Chung, and
Alain Bensoussan
(2017)
A Prediction Model
Guided Jaya Algorithm
for the PV System
Maximum Power Point
Tracking
This paper proposes a novel
model-free solution algorithm, the
natural cubic spline guided Jaya
algorithm (S-Jaya), for efficiently
solving the maximum power point
tracking (MPPT) problem of PV
systems under partial shading
conditions.
The Jaya algorithm, a variant of the
swarm intelligence, did
not require algorithm-specific
parameters and this advantage
made it an attractive solution
algorithm for the MPPT of PV
systems compared with other
heuristic search algorithms.
3. Karim Dahech, Moez
Allouche, Tarak
Damak, Fernando
Tadeo
(2017)
Backstepping sliding
mode control for
maximum power point
tracking of a
photovoltaic system
In this paper, a fast and efficient
maximum power point tracking
(MPPT) control for a photovoltaic
(PV)system is developed based on
both backstepping and sliding
mode approaches.
The proposed approach presents a
good tran-sition response without
overshoot, a low tracking error and
very afast system reaction against
solar irradiation change.

6. Literature Survey
Sr no Name of authors
and year
Title of paper Methodology Claim by author
4. J. Prasanth Ram, N.
Rajasekar
(2017)
A new robust,
mutated and fast
tracking LPSO
method for solar PV
maximum power
point tracking under
partial shaded
conditions
In this paper, a global maximum power
point tracking (GMPPT) algorithm
based on Leader Particle Swarm
Optimization (LPSO) is proposed for
PV system.
Being penalized by deficient
randomness, the conventional
methods like P&O, INC and swarm
optimized PSO methods are abortive
under critical shade conditions, but
the benefit of mutations present in
LPSO helps the method to rectify the
recuperation to attain global peak
even under vital conditions.
5. Sadeq D. Al-Majidi
a,b, Maysam F.
Abbod a, Hamed S.
Al-Raweshidy
(2018)
A novel maximum
power point tracking
technique
based on fuzzy logic
for photovoltaic
systems
In this paper, a novel MPPT technique
based on FL control and P&O
algorithm is presented. The proposed
method incorporates the advantages of
the P&O-MPPT to account for slow
and fast changes in solar irradiance and
the reduced processing time for the
FL-MPPT to address complex
engineering problems when the
membership functions are few.
That is, the proposed concept has
been demonstrated to be highly
effective for working with a grid
connected PV system, achieving
efficiencies of around 99.6%.
6. Milad Bahrami,
Roghayeh
Gavagsaz-
Ghoachani, Majid
Zandi
(2019)
Hybrid maximum
power point tracking
algorithm
with improved
dynamic performance
Here, a new hybrid method is proposed
for maximum power point tracking
(MPPT). The fuzzy logic control-based
method is very fast and accurate but
difficult to implement.
In this paper, the 3-point weight
method has been combined with the
fuzzy logic method in order to
increase the speed of MPPT.

7. LITERATURE SURVEY
Sr no Name of authors
and year
Title of paper Methodology Claim by author
7. Muhammad Nizar
Habibi, Novie Ayub
Windarko, Anang
Tjahjono
(2019)
Hybrid Maximum
Power Point Tracking
Using
Artificial Neural
Network-Incremental
Conduction
With Short Circuit
Current of Solar
Panel
In this paper, the type of MPPT used is
MPPT hybrid, which is a combination
of Artificial Neural Network based
MPPT and Incremental Conductance
algorithm. The value of short circuit
current from the solar panel is used as
an ANN reference to reach of
maximum power from the solar panel.
Incremental conductance is used to
keep the maximum power
The proposed MPPT was fast to
reach MPP, could find the MPP from the
solar panel, and could adapt to variations
in solar irradiation. The proposed
algorithm can reach MPP with a time of
0.057 s at 1000 W/m2 solar irradiation,
can increase 9.4% from the tracking result
of MPPT ANN at irradiation 500 W/m2
and can produce energy of 743.70 Ws for
2 seconds.
8. Xiaoshun Zhang,
Shengnan Li, Tingyi
He, Bo Yang , Tao
Yu
(2019)
Memetic
reinforcement
learning based
maximum power
point
tracking design for
PV systems under
partial shading
condition
This paper proposes a novel memetic
reinforcement learning (MRL) based
MPPT scheme for photovoltaic (PV)
systems under partial shading condition
(PSC). In order to enhance the
searching ability of MRL, the memetic
computing structure is incorporated
into reinforcement learning (RL).
Through incorporating the memetic
computing framework into RL, the
searching ability of MRL can be
significantly enhanced based on an
effective coordination between the local
search and the global information
exchange.
9. Mohammad Babaie,
Mohammad
Sharifzadeh1, Majid
Mehrasa
(2020)
PV Panels Maximum
Power Point Tracking
based on ANN in
Three-Phase Packed
E-Cell Inverter
This manuscript introduces a novel
Maximum Power Point Tracking
(MPPT) technique based on Artificial
Neural Network (ANN) to inject
harvested electrical power from PV
panels to a three-phase stand-alone load
using nine-level Packed E-Cell (PEC9)
inverter.
Using the proposed ANN-MPPT
controller, PI controllers used in
Conventional MPPT algorithms have
been removed; so, the MPPT control loop
design has been simplified.

8. Problem Definition
 Generally, the converter that to be used is a type of Boost. The boost converter
works when input voltage smaller than desired output voltage.
 When the voltage of the PV is greater than the desired voltage, boost converter
will not work properly and maximum power from the PV is not achieved.
 Selection of the type converter Buck- Boost, Cuk, Sepic and Zeta are to optimize
the PV power output.

9. The main objective of propose methodologies are as follows:
•Comparison study of the Buck-boost, Cuk, Sepic and Zeta converters as
a device of MPPT.
•Observation of converter outputs ripple current, voltage on input side
and output side of converter, input power and output power for best
converter identification.
•Identification of best converter at different solar irradiation and
temperature condition on solar pv system.
•Design of incremental conduction method based Maximum power point
tracking (MPPT) algorithm and analysis of this MPPT technique with
Buck-boost, Cuk, Sepic and Zeta converters.
Objectives

10. Proposed Methodology
Figure 1: Block diagram of proposed of photovoltaic module system

11. Project Implementation
The complete system will be design on MATLAB 2015a software in simulink
environment using different toolbox.
 Design of solar pv system using sim-power system toolbox.
 Design of DC-DC different types of converter using sim-power electronics
toolbox.
 Design of MPPT algorithm using general purpose toolbox and mathematical
toolbox.
 Design of measurement subsystem for solar PV output analysis using sim-
power system toolbox.

12. PROJECT SHEDULE
Sr.n
o. Month Activity Work
1 November 2021 Topic finalization To finalization of project topic,
preparation of synopsis and literature survey
2 November 2021 Literature review Study of different types of MPPT and DC-
DC converters for solar PV system.
3 December 2021 Synopsis report Complete idea of project
4 December 2021 Synopsis report submission Complete overview of project and technique
finalization.
5 December 2022 Progress seminar1 Project working and implementation idea
6 January 2022 Progress seminar2 Simulation model implementation and
parameter specification.
7 February 2022 Progress seminar3 Comparisons, suggestions based on the
comparisons.
8 March 2022 Pre-submission seminar Pre-submission seminar on complete work
with result
9 April 2022 Submission of thesis and hard
copy
Submission of thesis hard copy April 1st
week.

13. REFERANCES
[1] Shringi, S., Sharma, S. K., & Rathode, K. S. (2019, October). Comparative study of buck-
boost, Cuk and Zeta converter for maximum output power using P&O technique with solar.
In 2019 2nd International Conference on Power Energy, Environment and Intelligent Control
(PEEIC) (pp. 538-542). IEEE. (base paper)
[2] Soufi, Y., Bechouat, M., & Kahla, S. (2017). Fuzzy-PSO controller design for maximum
power point tracking in photovoltaic system. International Journal of hydrogen energy, 42(13),
8680-8688.
[3] Huang, C., Wang, L., Yeung, R. S. C., Zhang, Z., Chung, H. S. H., & Bensoussan, A. (2017).
A prediction model-guided Jaya algorithm for the PV system maximum power point
tracking. IEEE Transactions on sustainable energy, 9(1), 45-55.
[4] Dahech, K., Allouche, M., Damak, T., & Tadeo, F. (2017). Backstepping sliding mode control
for maximum power point tracking of a photovoltaic system. Electric Power Systems
Research, 143, 182-188.
[5] Ram, J. P., & Rajasekar, N. (2017). A new robust, mutated and fast tracking LPSO method for
solar PV maximum power point tracking under partial shaded conditions. Applied energy, 201,
45-59.

14. [6] Al-Majidi, S. D., Abbod, M. F., & Al-Raweshidy, H. S. (2018). A novel maximum power
point tracking technique based on fuzzy logic for photovoltaic systems. International Journal of
Hydrogen Energy, 43(31), 14158-14171.
[7] Bahrami, M., Gavagsaz-Ghoachani, R., Zandi, M., Phattanasak, M., Maranzanaa, G., Nahid-
Mobarakeh, B., ... & Meibody-Tabar, F. (2019). Hybrid maximum power point tracking algorithm
with improved dynamic performance. Renewable energy, 130, 982-991.
[8] Habibi, M. N., Windarko, N. A., & Tjahjono, A. (2019, September). Hybrid Maximum Power
Point Tracking Using Artificial Neural Network-Incremental Conduction With Short Circuit
Current of Solar Panel. In 2019 International Electronics Symposium (IES) (pp. 63-69). IEEE.
[9] Zhang, X., Li, S., He, T., Yang, B., Yu, T., Li, H., ... & Sun, L. (2019). Memetic reinforcement
learning based maximum power point tracking design for PV systems under partial shading
condition. Energy, 174, 1079-1090.
[10] Babaie, M., Sharifzadeh, M., Mehrasa, M., Chouinard, G., & Al-Haddad, K. (2020,
February). PV panels maximum power point tracking based on ANN in three-phase packed e-cell
inverter. In 2020 IEEE International Conference on Industrial Technology (ICIT) (pp. 854-859).
IEEE
REFERANCES

15. Thank You