Optimization of electrical wiring design in buildings using particle swarm optimization and genetic algorithm / Tuan Ahmad Fauzi Tuan Abdullah

In Malaysia, the number of population in the cities is increasing due to urbanization and job opportunities. As a result, the number of high rise building is also increasing. Hence, electrical system is becoming crucial in the construction of high rise building so that the building could be occupied...

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Main Author: Tuan Ahmad Fauzi, Tuan Abdullah
Format: Thesis
Published: 2017
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Online Access:http://studentsrepo.um.edu.my/8543/4/KMA150009.pdf
http://studentsrepo.um.edu.my/8543/
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author Tuan Ahmad Fauzi, Tuan Abdullah
author_facet Tuan Ahmad Fauzi, Tuan Abdullah
author_sort Tuan Ahmad Fauzi, Tuan Abdullah
building UM Library
collection Institutional Repository
content_provider Universiti Malaya
content_source UM Student Repository
continent Asia
country Malaysia
description In Malaysia, the number of population in the cities is increasing due to urbanization and job opportunities. As a result, the number of high rise building is also increasing. Hence, electrical system is becoming crucial in the construction of high rise building so that the building could be occupied safely and comfortably by tenants or residences. Commonly, electrical system is designed based on the customers’ requirements and it must comply according to certain requirements and regulation from authorities or standard bodies. The electrical wiring system design includes sizing of cables and bus ducts, customers’ load and placement of load, cables and bus ducts. Therefore, these parameters have to be emphasized on the planning stage. In this project, the main objective is to optimize the electrical distribution system design in buildings using optimization methods, which are Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The main reasons of using these optimization methods is to propose a minimum total cost and lowest voltage drop of electrical system design in buildings.Comparison between the optimisation methods and without using optimisation methods show that the total cost and total voltage drop are lower when using optimisation methods. Comparison between the results using PSO and GA shows that both methods yield the same total cost and total voltage drop but GA yields consistent results compared to PSO. Therefore, GA is more suitable than PSO in finding the lowest total voltage drop and total cost when designing an electrical system in a building.
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spelling my.um.stud-85432020-09-21T20:15:04Z Optimization of electrical wiring design in buildings using particle swarm optimization and genetic algorithm / Tuan Ahmad Fauzi Tuan Abdullah Tuan Ahmad Fauzi, Tuan Abdullah T Technology (General) TK Electrical engineering. Electronics Nuclear engineering In Malaysia, the number of population in the cities is increasing due to urbanization and job opportunities. As a result, the number of high rise building is also increasing. Hence, electrical system is becoming crucial in the construction of high rise building so that the building could be occupied safely and comfortably by tenants or residences. Commonly, electrical system is designed based on the customers’ requirements and it must comply according to certain requirements and regulation from authorities or standard bodies. The electrical wiring system design includes sizing of cables and bus ducts, customers’ load and placement of load, cables and bus ducts. Therefore, these parameters have to be emphasized on the planning stage. In this project, the main objective is to optimize the electrical distribution system design in buildings using optimization methods, which are Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The main reasons of using these optimization methods is to propose a minimum total cost and lowest voltage drop of electrical system design in buildings.Comparison between the optimisation methods and without using optimisation methods show that the total cost and total voltage drop are lower when using optimisation methods. Comparison between the results using PSO and GA shows that both methods yield the same total cost and total voltage drop but GA yields consistent results compared to PSO. Therefore, GA is more suitable than PSO in finding the lowest total voltage drop and total cost when designing an electrical system in a building. 2017 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/8543/4/KMA150009.pdf Tuan Ahmad Fauzi, Tuan Abdullah (2017) Optimization of electrical wiring design in buildings using particle swarm optimization and genetic algorithm / Tuan Ahmad Fauzi Tuan Abdullah. Masters thesis, University of Malaya. http://studentsrepo.um.edu.my/8543/
spellingShingle T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Tuan Ahmad Fauzi, Tuan Abdullah
Optimization of electrical wiring design in buildings using particle swarm optimization and genetic algorithm / Tuan Ahmad Fauzi Tuan Abdullah
title Optimization of electrical wiring design in buildings using particle swarm optimization and genetic algorithm / Tuan Ahmad Fauzi Tuan Abdullah
title_full Optimization of electrical wiring design in buildings using particle swarm optimization and genetic algorithm / Tuan Ahmad Fauzi Tuan Abdullah
title_fullStr Optimization of electrical wiring design in buildings using particle swarm optimization and genetic algorithm / Tuan Ahmad Fauzi Tuan Abdullah
title_full_unstemmed Optimization of electrical wiring design in buildings using particle swarm optimization and genetic algorithm / Tuan Ahmad Fauzi Tuan Abdullah
title_short Optimization of electrical wiring design in buildings using particle swarm optimization and genetic algorithm / Tuan Ahmad Fauzi Tuan Abdullah
title_sort optimization of electrical wiring design in buildings using particle swarm optimization and genetic algorithm / tuan ahmad fauzi tuan abdullah
topic T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
url http://studentsrepo.um.edu.my/8543/4/KMA150009.pdf
http://studentsrepo.um.edu.my/8543/
url_provider http://studentsrepo.um.edu.my/