Implimentation of evolutionary particle swarm optimization in distributed generation sizing

The size of Distributed Generation (DG) is crucial in order to reduce the impact of installing a DG in the distribution Network. Without proper connection and sizing of DG, it will cause the power loss to increase and also might cause the voltage in the network to operate beyond the acceptable limit...

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Main Authors: Jamian, Jasrul Jamani, Mustafa, Mohd. Wazir, Mokhlis, H., Baharudin, Muhammad Ariff
Format: Article
Published: Insitute of Advanced Engineeering and Science 2012
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Online Access:http://eprints.utm.my/id/eprint/30553/
http://dx.doi.org/10.11591/ijece.v2i1.227
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Institution: Universiti Teknologi Malaysia
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spelling my.utm.305532019-07-23T09:01:38Z http://eprints.utm.my/id/eprint/30553/ Implimentation of evolutionary particle swarm optimization in distributed generation sizing Jamian, Jasrul Jamani Mustafa, Mohd. Wazir Mokhlis, H. Baharudin, Muhammad Ariff TK Electrical engineering. Electronics Nuclear engineering The size of Distributed Generation (DG) is crucial in order to reduce the impact of installing a DG in the distribution Network. Without proper connection and sizing of DG, it will cause the power loss to increase and also might cause the voltage in the network to operate beyond the acceptable limit. Therefore, many researchers have given concentration on the formulation optimization technique to regulate the DG’s output to compute its optimal size. The distinctions between these techniques were on the ability to acquire the optimal value with hasty computing time for solving the problems. PSO is among the popular optimization methods due to its simplicity and satisfying value. However, the computing time for PSO is dependant to the problem that needs to be solved. In this paper, the concept of Evolutionary Particle Swarm Optimization (EPSO) method is implemented in sizing the DG units. By substituting the concept of Evolutionary Programming (EP) in some part of Particle Swarm Optimization (PSO) algorithm process, it will make the process of convergence become faster. The algorithm has been tested in 33bus distribution system with 3 units of DG that operate in PV mode. Its performance was compared with the performance when using the traditional PSO and without using any optimization method. In terms of power loss reduction and voltage profile, the EPSO can give similar performance as PSO. Moreover, the EPSO requires less number of iteration and computing time to converge. Thus, it can be said that the EPSO is superior in term of speed, while maintaining the same performance. Insitute of Advanced Engineeering and Science 2012-02 Article PeerReviewed Jamian, Jasrul Jamani and Mustafa, Mohd. Wazir and Mokhlis, H. and Baharudin, Muhammad Ariff (2012) Implimentation of evolutionary particle swarm optimization in distributed generation sizing. International Journal of Electrical and Computer Engineering (IJECE), 2 (1). pp. 137-146. ISSN 2088-8708 http://dx.doi.org/10.11591/ijece.v2i1.227 DOI:10.11591/ijece.v2i1.227
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Jamian, Jasrul Jamani
Mustafa, Mohd. Wazir
Mokhlis, H.
Baharudin, Muhammad Ariff
Implimentation of evolutionary particle swarm optimization in distributed generation sizing
description The size of Distributed Generation (DG) is crucial in order to reduce the impact of installing a DG in the distribution Network. Without proper connection and sizing of DG, it will cause the power loss to increase and also might cause the voltage in the network to operate beyond the acceptable limit. Therefore, many researchers have given concentration on the formulation optimization technique to regulate the DG’s output to compute its optimal size. The distinctions between these techniques were on the ability to acquire the optimal value with hasty computing time for solving the problems. PSO is among the popular optimization methods due to its simplicity and satisfying value. However, the computing time for PSO is dependant to the problem that needs to be solved. In this paper, the concept of Evolutionary Particle Swarm Optimization (EPSO) method is implemented in sizing the DG units. By substituting the concept of Evolutionary Programming (EP) in some part of Particle Swarm Optimization (PSO) algorithm process, it will make the process of convergence become faster. The algorithm has been tested in 33bus distribution system with 3 units of DG that operate in PV mode. Its performance was compared with the performance when using the traditional PSO and without using any optimization method. In terms of power loss reduction and voltage profile, the EPSO can give similar performance as PSO. Moreover, the EPSO requires less number of iteration and computing time to converge. Thus, it can be said that the EPSO is superior in term of speed, while maintaining the same performance.
format Article
author Jamian, Jasrul Jamani
Mustafa, Mohd. Wazir
Mokhlis, H.
Baharudin, Muhammad Ariff
author_facet Jamian, Jasrul Jamani
Mustafa, Mohd. Wazir
Mokhlis, H.
Baharudin, Muhammad Ariff
author_sort Jamian, Jasrul Jamani
title Implimentation of evolutionary particle swarm optimization in distributed generation sizing
title_short Implimentation of evolutionary particle swarm optimization in distributed generation sizing
title_full Implimentation of evolutionary particle swarm optimization in distributed generation sizing
title_fullStr Implimentation of evolutionary particle swarm optimization in distributed generation sizing
title_full_unstemmed Implimentation of evolutionary particle swarm optimization in distributed generation sizing
title_sort implimentation of evolutionary particle swarm optimization in distributed generation sizing
publisher Insitute of Advanced Engineeering and Science
publishDate 2012
url http://eprints.utm.my/id/eprint/30553/
http://dx.doi.org/10.11591/ijece.v2i1.227
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