Optimal location and sizing of distributed generation using particle swarm optimization with mutation strategy

The current energy crisis has led to the increasing demand of environmental-friendly and high efficient energy. On top of all the solutions, distributed generation (DG) is one of the solutions that is capable to overcome this problem. The impact of DG towards the distribution system is significan...

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Main Author:	Wong, Lye Yee
Format:	Thesis
Language:	English
Subjects:	Distributed generation Loss minimization Particle swarm optimzation Distribution system
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33131/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33131/2/Full%20text.pdf
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id	my-unimap-33131
record_format	uketd_dc
spelling	my-unimap-331312014-03-26T03:43:45Z Optimal location and sizing of distributed generation using particle swarm optimization with mutation strategy Wong, Lye Yee The current energy crisis has led to the increasing demand of environmental-friendly and high efficient energy. On top of all the solutions, distributed generation (DG) is one of the solutions that is capable to overcome this problem. The impact of DG towards the distribution system is significant where it can be used to improve the system reliability and efficiency such as improving the voltage profile, reducing the total power losses, etc. The optimal location and size of DG is very important in order to obtain the maximum output from the DG allocation. Many researchers found out that solutions using metaheuristic methods yield a better result compared to the conventional analytical method. In this thesis, the Particle Swarm Optimization (PSO) combined with the mutation strategy (PSO-MS) method is proposed in solving the DG allocation problem with the purpose of minimizing the total real power loss and improving the voltage profile of the system. This is to prevent the stagnancy of the particles’ population that usually happens in PSO algorithm. A set of comprehensive simulations have been carried out to validate the performance of the proposed method where they are categorized into small system (24-bus distribution system), medium system (33-bus distribution system), and large system (69-bus distribution system) for single DG and 2 DGs installation. The simulation results of the PSO-MS method are then compared with PSO and Genetic Algorithm (GA) method in order to validate the performance of the proposed method. From the results, it is shown that the proposed method has successfully obtained the optimal DG location and size. As for the comparative study with PSO and GA, the PSO-MS method also yields a better performance in terms of total real power loss, voltage profile and simulation time. Universiti Malaysia Perlis (UniMAP) 2011 Thesis en http://dspace.unimap.edu.my:80/dspace/handle/123456789/33131 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33131/1/Page%201-24.pdf c39af976392d1389cf883780b8465237 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33131/2/Full%20text.pdf 408735ceaea358e8d7c3566d07043371 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33131/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Distributed generation Loss minimization Particle swarm optimzation Distribution system School of Electrical Systems Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
topic	Distributed generation Loss minimization Particle swarm optimzation Distribution system
spellingShingle	Distributed generation Loss minimization Particle swarm optimzation Distribution system Wong, Lye Yee Optimal location and sizing of distributed generation using particle swarm optimization with mutation strategy
description	The current energy crisis has led to the increasing demand of environmental-friendly and high efficient energy. On top of all the solutions, distributed generation (DG) is one of the solutions that is capable to overcome this problem. The impact of DG towards the distribution system is significant where it can be used to improve the system reliability and efficiency such as improving the voltage profile, reducing the total power losses, etc. The optimal location and size of DG is very important in order to obtain the maximum output from the DG allocation. Many researchers found out that solutions using metaheuristic methods yield a better result compared to the conventional analytical method. In this thesis, the Particle Swarm Optimization (PSO) combined with the mutation strategy (PSO-MS) method is proposed in solving the DG allocation problem with the purpose of minimizing the total real power loss and improving the voltage profile of the system. This is to prevent the stagnancy of the particles’ population that usually happens in PSO algorithm. A set of comprehensive simulations have been carried out to validate the performance of the proposed method where they are categorized into small system (24-bus distribution system), medium system (33-bus distribution system), and large system (69-bus distribution system) for single DG and 2 DGs installation. The simulation results of the PSO-MS method are then compared with PSO and Genetic Algorithm (GA) method in order to validate the performance of the proposed method. From the results, it is shown that the proposed method has successfully obtained the optimal DG location and size. As for the comparative study with PSO and GA, the PSO-MS method also yields a better performance in terms of total real power loss, voltage profile and simulation time.
format	Thesis
author	Wong, Lye Yee
author_facet	Wong, Lye Yee
author_sort	Wong, Lye Yee
title	Optimal location and sizing of distributed generation using particle swarm optimization with mutation strategy
title_short	Optimal location and sizing of distributed generation using particle swarm optimization with mutation strategy
title_full	Optimal location and sizing of distributed generation using particle swarm optimization with mutation strategy
title_fullStr	Optimal location and sizing of distributed generation using particle swarm optimization with mutation strategy
title_full_unstemmed	Optimal location and sizing of distributed generation using particle swarm optimization with mutation strategy
title_sort	optimal location and sizing of distributed generation using particle swarm optimization with mutation strategy
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Electrical Systems Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33131/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33131/2/Full%20text.pdf
_version_	1747836803102015488

Optimal location and sizing of distributed generation using particle swarm optimization with mutation strategy

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