This document presents a project on developing an artificial intelligence technique based maximum power point tracking (MPPT) controller for a photovoltaic system. The project aims to study the performance of an AI-based MPPT controller and compare it to other MPPT controller types. It involves collecting system operation data under varying irradiation and temperature conditions. Simulation results are presented comparing the particle swarm optimization and perturb & observe MPPT algorithms. The conclusions are that the PSO-based controller has faster steady state and transient response and higher overall efficiency compared to the P&O controller. Future work areas discussed are developing MPPT controller apps, integrating DC loads, and further optimizing the algorithms.

1. Under the Supervision of
Mr. SATYAM UPADHYAY
Department of Electrical Engineering
Uma Nath Singh Institute of Engineering & Technology
VEER BAHADUR SINGH PURVANCHAL UNIVERSITY
JAUNPUR, UTTAR PRADESH, INDIA
[2017-2021]
ARTIFICIAL INTELLIGENCE TECHNIQUE BASED MAXIMUM POWER
POINT TRACKING CONTROLLER FOR PHOTO-VOLTAIC SYSTEM
A Project Presentation Submitted in Partial Fulfillment of the Requirement
for the Award of the Degree of
BACHELOR OF TECHNOLOGY
In
ELECTRICAL ENGINEERING
Presented By
ANURAG KUMAR (ID No.175616)
NITESH KUMAR BIND (ID No. 175632)

2. CONTENT
 Introduction
 MPPT Controller
 Working
 Characteristics of Solar Module
 Main Model
 Perturb & Observe Approach
 Particle Swarm Optimization
 Waveform
 Future Work
 Conclusion
 References

3. Introduction
Objective
The main objective of the project is to study the performance of AI
based MPPT controller for solar photovoltaic cell and compare its result
with other types of MPPT controller.
• To collect proper data and synthesis data that describes the system
operation.
• Performance of MPPT controller should be checked at varying
irradiation and temperature
Organization of Project
• First chapter provides the introduction of the project and fundamental
concept
• Second chapter contains review of research work
• Third chapter provides the basic concept of PV cell and different type
of MPPT controller
• Fourth chapter contain simulation result performed on MATLAB
• Fifth chapter provides summary and conclusion of the project

4.  A.I. based controller
 Operates at a very high frequency
 Increase the efficiency of power generated by
the solar panel and decrease the line losses.
 In daylight sun intensity is so high so it extracts
maximum amount of solar radiation.
 Different algorithm was proposed to enhance
the performance of MPPT algorithm i.e. Perturb
& Observe, Incremental Conductance, and there
are some AI algorithms also i.e. Particle Swarm
Optimization, ANN, Fuzzy logic
M.P.P.T. Controller

5. Working
 Solar panel extracts solar energy
from photo-voltaic cell
 The extracted energy is pass to
chopper
 In solar panel V-I controller is
attached with MPPT controller and
forming a closed loop system with
chopper.
 The output of generated power is
connected with load.

6. Characteristics of Solar module
Fig: Effect of Irradiance
Fig: Effect of Temperature

7. Main Model

8. Perturb & ObserveApproach
• Widely used technique and broadly acknowledge by MPPT
controller
• This technique works on repeatedly increasing or decreasing
the photovoltaic system voltage and compares it with its
previous value
• It has slow response and oscillates around MPP which cause
excessive watt loss
• Mainly use for commercial purpose because it is easy to run
• Less suitable for varying irradiation and at low temperature
condition

9. Particle Swarm Optimization
Approach
• Advance version of P&O
• Idea came from bird schooling
• It can easily locate maximum locus point in
solar PV cell and extract maximum amount of
power from it even in varying irradiance and
temperature
• The steady state and transient response of PSO
based MPPT controller is very fast
• The overall efficiency of PSO based MPPT
controller is high

10. Waveform
Fig: Output Voltage of Boost Converter in Case of P&O
Fig: Output Voltage of Boost Converter in Case of PSO

11. Future Work
• MPPT operating APP: An application of operating
MPPT by the help of smartphones can so be made
operate from whenever via the Internet.
• DC-DC running loads: DC from MPPT can be taken
directly and DC load can be run. DC loads helps to
consume low electricity.
• Energy Management: There is need to manage energy
when these algorithm are developed
• Future work will try to understand where the algorithm
suffers in order to understand any limitations and apply
it to the proper contexts.

12. Conclusion
• The overall goal is to investigate the operation of a
MPPT Controller for solar PV module using Particle
Swarm Optimization and Perturb and Observe
algorithms.
• The artificial intelligence technique based MPPT
controller is used to control the MPP of the PV system.
• The steady state and transient response of PSO based
MPPT controller is fast in comparison with P&O based
MPPT controller

13. References
[1 ]Anurag Kumar, Anurag Singh, “The Growing Need of renewable
Energy in India,” 2nd International Conference on “Advances in
Electronics, Electrical and Computational Intelligence – 2021” at IIIT
Allahabad
[2]U. Sharma, S. Kumar, and B. Singh, “Solar array fed water pump in
system using induction motor drive,” 1st IEEE Int. Conf. Power
Electron. Intell. Control Energy Syst. ICPEICES 2016, 2017.
[3]C. S. Chin, P. Neelakantan, H. P. Yoong, and K. T. K. Teo,
“Optimisation of Fuzzy Based Maximum Power Point Tracking in Pv
System for Rapidly Changing Solar Irradiance,” no. June, 2011.
[4]D. P. Hohm, “Comparative Study of Maximum Power Point Tracking
Algorithms Using an Experimental, Programmable, Maximum Power
Point Tracking Test Bed,” 2000.
[5]M. A. G. De Brito, L. P. Sampaio, L. G. Jr, G. A. Melo, and C. A.
Canesin, “Comparative Analysis of MPPT Techniques for PV
Applications,” pp. 99–104, 2011.
[6]E. A. Jasmin and J. James, “Implementation of Fuzzy Logic based
Maximum Power Point Tracking in Photovoltaic System.”

14. Thank You