Optimal static var compensator placement for voltage stability improvement using particle swarm optimization technique

Electricity demand has grown rapidly every year these days. The growing demand gives the power system a pressure, but it can be solved by increasing more power and extending the line of the transmission. But, the use of this solution is opposed to some factors such as environmental and economic fact...

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Main Author: Ahmad Munzir Mohd Shamsul Bahrin
Format: text::Final Year Project
Language:English US
Published: 2023
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spelling my.uniten.dspace-274342023-12-07T16:40:52Z Optimal static var compensator placement for voltage stability improvement using particle swarm optimization technique Ahmad Munzir Mohd Shamsul Bahrin Electric power system stability Mathematical optimization Swarm intelligence Electricity demand has grown rapidly every year these days. The growing demand gives the power system a pressure, but it can be solved by increasing more power and extending the line of the transmission. But, the use of this solution is opposed to some factors such as environmental and economic factors. These factors have been considered for further research into the design of power networks that can deliver maximum capacity of transmission at minimum cost. The study of this project is regarding the optimization technique called Particle Swarm Optimization (PSO). this proposed optimization technique used to solve the problem of SVC installation. For this research, the IEEE 26 Bus Reliability Test (RTS) is used to test the proposed optimization technique efficiencies. 2023-06-19T01:01:48Z 2023-06-19T01:01:48Z 2018 Resource Types::text::Final Year Project https://irepository.uniten.edu.my/handle/123456789/27434 en_US 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 US
topic Electric power system stability
Mathematical optimization
Swarm intelligence
spellingShingle Electric power system stability
Mathematical optimization
Swarm intelligence
Ahmad Munzir Mohd Shamsul Bahrin
Optimal static var compensator placement for voltage stability improvement using particle swarm optimization technique
description Electricity demand has grown rapidly every year these days. The growing demand gives the power system a pressure, but it can be solved by increasing more power and extending the line of the transmission. But, the use of this solution is opposed to some factors such as environmental and economic factors. These factors have been considered for further research into the design of power networks that can deliver maximum capacity of transmission at minimum cost. The study of this project is regarding the optimization technique called Particle Swarm Optimization (PSO). this proposed optimization technique used to solve the problem of SVC installation. For this research, the IEEE 26 Bus Reliability Test (RTS) is used to test the proposed optimization technique efficiencies.
format Resource Types::text::Final Year Project
author Ahmad Munzir Mohd Shamsul Bahrin
author_facet Ahmad Munzir Mohd Shamsul Bahrin
author_sort Ahmad Munzir Mohd Shamsul Bahrin
title Optimal static var compensator placement for voltage stability improvement using particle swarm optimization technique
title_short Optimal static var compensator placement for voltage stability improvement using particle swarm optimization technique
title_full Optimal static var compensator placement for voltage stability improvement using particle swarm optimization technique
title_fullStr Optimal static var compensator placement for voltage stability improvement using particle swarm optimization technique
title_full_unstemmed Optimal static var compensator placement for voltage stability improvement using particle swarm optimization technique
title_sort optimal static var compensator placement for voltage stability improvement using particle swarm optimization technique
publishDate 2023
_version_ 1806426544201007104
score 13.214268