Neuro-Fuzzy Controller-Based Solar Panel Tracking System

Neuro-Fuzzy Controller-Based Solar Panel Tracking System

The demand for the use of renewable energy sources has increased considerably, in recent years, due to the fast depletion of fossil fuels and population growth. Among the renewable energy sources, the solar photovoltaic (PV) system is a popular and the most economical renewable energy source. Electr...

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Main Author: Hossain, Md Arif
Format: Thesis
Published: 2019
Subjects:
QA1-43 General
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id my-mmu-ep.7733
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spelling my-mmu-ep.77332020-09-18T05:42:44Z Neuro-Fuzzy Controller-Based Solar Panel Tracking System 2019-05 Hossain, Md Arif QA1-43 General The demand for the use of renewable energy sources has increased considerably, in recent years, due to the fast depletion of fossil fuels and population growth. Among the renewable energy sources, the solar photovoltaic (PV) system is a popular and the most economical renewable energy source. Electric power generation using solar PV system utilizes solar panels exposed to the sun. As the position of the sun changes throughout the day, solar tracking systems play a vital role in the efficient operation of a solar photovoltaic system. Therefore there is a need for an efficient controller for the tracking system to maximize the power output from the solar photovoltaic system. This thesis deals with the design and development of a smart controller based solar panel tracking system. Different types of controllers including P, PI, PID, and fuzzy logic controllers have been applied to the tracking system. However, the improvement is not found to be considerable. They have the disadvantage of being relatively hard to design and they may not work well for non-linear systems. This thesis proposes the design and development of a smart neuro fuzzy controller based solar panel tracking system. 2019-05 Thesis http://shdl.mmu.edu.my/7733/ http://library.mmu.edu.my/library2/diglib/mmuetd/ masters Multimedia University Faculty of Engineering & Technology
institution Multimedia University
collection MMU Institutional Repository
topic QA1-43 General
spellingShingle QA1-43 General
Hossain, Md Arif
Neuro-Fuzzy Controller-Based Solar Panel Tracking System
description The demand for the use of renewable energy sources has increased considerably, in recent years, due to the fast depletion of fossil fuels and population growth. Among the renewable energy sources, the solar photovoltaic (PV) system is a popular and the most economical renewable energy source. Electric power generation using solar PV system utilizes solar panels exposed to the sun. As the position of the sun changes throughout the day, solar tracking systems play a vital role in the efficient operation of a solar photovoltaic system. Therefore there is a need for an efficient controller for the tracking system to maximize the power output from the solar photovoltaic system. This thesis deals with the design and development of a smart controller based solar panel tracking system. Different types of controllers including P, PI, PID, and fuzzy logic controllers have been applied to the tracking system. However, the improvement is not found to be considerable. They have the disadvantage of being relatively hard to design and they may not work well for non-linear systems. This thesis proposes the design and development of a smart neuro fuzzy controller based solar panel tracking system.
format Thesis
qualification_level Master's degree
author Hossain, Md Arif
author_facet Hossain, Md Arif
author_sort Hossain, Md Arif
title Neuro-Fuzzy Controller-Based Solar Panel Tracking System
title_short Neuro-Fuzzy Controller-Based Solar Panel Tracking System
title_full Neuro-Fuzzy Controller-Based Solar Panel Tracking System
title_fullStr Neuro-Fuzzy Controller-Based Solar Panel Tracking System
title_full_unstemmed Neuro-Fuzzy Controller-Based Solar Panel Tracking System
title_sort neuro-fuzzy controller-based solar panel tracking system
granting_institution Multimedia University
granting_department Faculty of Engineering & Technology
publishDate 2019
_version_ 1747829669427675136

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