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Optimal sizing and placement of renewable energy system for minimum power loss and voltage improvement in power system using genetic algorithm

TK1006.M83 2017

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Main Author: Muhammad Aniq-Aiman Kamalluddin
Format: text::Final Year Project
Language:English
Published: 2024
Subjects:
Distributed generation of electric power
Renewable energy sources
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spelling my.uniten.dspace-330002024-08-04T02:01:02Z Optimal sizing and placement of renewable energy system for minimum power loss and voltage improvement in power system using genetic algorithm Muhammad Aniq-Aiman Kamalluddin Distributed generation of electric power Renewable energy sources TK1006.M83 2017 Nowadays, electrical power demands increase rapidly and become unpredictable causing the company of electrical power supply facing problems such as power system losses with voltage instability and affecting reliability, power quality indices and cost. Therefore, the Distributed Generation (DG) has then become the most suitable and preferable solution especially renewable energy type due to its advantages. In order to install this DG at distribution line, the location and sizing of the DG is crucial to avoid other power system issues like overvoltage and instability. This thesis reports a development to find the most optimum placement and sizing of DG to minimize power losses and improve voltage stability. To determine the placement of the DG, Fast Voltage Stability Index (FVSI) becomes crucial to identify the condition and state of the bus system. Furthermore, to decide the sizing of the DG, artificial intelligence comes as a perfect method as it best in their capability for real time control, simpler and faster calculations and flexibility to various operating circumstances to obtain the most optimized value. Hence, in this research paper, genetic algorithm (GA) will be used to find the proper sizing of the DG. Power flow analysis then becomes the next-key analysis to evaluate the voltage magnitude and the line losses of the bus before and after the DG installation. Observations and research are done on IEEE 33 radial bus system using Matlab environment. 2024-07-30T07:41:41Z 2024-07-30T07:41:41Z 2017 Resource Types::text::Final Year Project https://irepository.uniten.edu.my/handle/123456789/33000 en application/pdf
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
topic Distributed generation of electric power
Renewable energy sources
spellingShingle Distributed generation of electric power
Renewable energy sources
Muhammad Aniq-Aiman Kamalluddin
Optimal sizing and placement of renewable energy system for minimum power loss and voltage improvement in power system using genetic algorithm
description TK1006.M83 2017
format Resource Types::text::Final Year Project
author Muhammad Aniq-Aiman Kamalluddin
author_facet Muhammad Aniq-Aiman Kamalluddin
author_sort Muhammad Aniq-Aiman Kamalluddin
title Optimal sizing and placement of renewable energy system for minimum power loss and voltage improvement in power system using genetic algorithm
title_short Optimal sizing and placement of renewable energy system for minimum power loss and voltage improvement in power system using genetic algorithm
title_full Optimal sizing and placement of renewable energy system for minimum power loss and voltage improvement in power system using genetic algorithm
title_fullStr Optimal sizing and placement of renewable energy system for minimum power loss and voltage improvement in power system using genetic algorithm
title_full_unstemmed Optimal sizing and placement of renewable energy system for minimum power loss and voltage improvement in power system using genetic algorithm
title_sort optimal sizing and placement of renewable energy system for minimum power loss and voltage improvement in power system using genetic algorithm
publishDate 2024
_version_ 1806517981564370944
score 13.222552

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