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
Published: Universiti Malaysia Perlis (UniMAP) 2014
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Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/33131
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Institution: Universiti Malaysia Perlis
Language: English
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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 Distributed generation Loss minimization Particle swarm optimzation Distribution system 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. 2014-03-26T03:43:45Z 2014-03-26T03:43:45Z 2011 Thesis http://dspace.unimap.edu.my:80/dspace/handle/123456789/33131 en Universiti Malaysia Perlis (UniMAP) School of Electrical Systems Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
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
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/33131
_version_ 1643797077529985024