Comprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reduction

With the technological advancements, distributed generation (DG) has become a common method of overwhelming the issues like power losses and voltage drops which accompanies with the leaf of the feeders of radial distribution networks. Many researchers have used several optimization techniques and to...

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Main Authors: Karunarathne E., Pasupuleti J., Ekanayake J., Almeida D.
Other Authors: 57216633155
Format: Article
Published: Institute of Advanced Engineering and Science 2023
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Institution: Universiti Tenaga Nasional
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spelling my.uniten.dspace-252162023-05-29T16:07:24Z Comprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reduction Karunarathne E. Pasupuleti J. Ekanayake J. Almeida D. 57216633155 11340187300 7003409510 57211718103 With the technological advancements, distributed generation (DG) has become a common method of overwhelming the issues like power losses and voltage drops which accompanies with the leaf of the feeders of radial distribution networks. Many researchers have used several optimization techniques and tools which could be used to locate and size the DG units in the system. Particle swarm optimization (PSO) is one of the famous optimization techniques. However, the premature convergence is identified as a fundamental adverse effect of this optimization technique. Therefore, the optimization problem can direct the objective function to a local minimum. This paper presents a variant of PSO techniques, "comprehensive learning particle swarm optimization (CLPSO)"to determine the optimal placement and sizing of the DGs, which uses a novel learning strategy whereby all other particles' historical best information and learning probability value are used to update a particle's velocity. The CLPSO particles learn from one exampler for few iterations, instead of learing from global and personal best values in every iteration in PSO and this technique retains the swarm's variability to avoid premature convergence. A detailed analysis was conducted for the IEEE 33 bus system. The comparison results have revealed a higher convergence and an accuracy than the PSO. Copyright � 2020 Institute of Advanced Engineering and Science. All rights reserved. Final 2023-05-29T08:07:24Z 2023-05-29T08:07:24Z 2020 Article 10.11591/ijeecs.v20.i1.pp16-23 2-s2.0-85088249266 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088249266&doi=10.11591%2fijeecs.v20.i1.pp16-23&partnerID=40&md5=67fd157e7b8b11157d27d6c951752e3b https://irepository.uniten.edu.my/handle/123456789/25216 20 1 16 23 All Open Access, Gold, Green Institute of Advanced Engineering and Science Scopus
institution Universiti Tenaga Nasional
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description With the technological advancements, distributed generation (DG) has become a common method of overwhelming the issues like power losses and voltage drops which accompanies with the leaf of the feeders of radial distribution networks. Many researchers have used several optimization techniques and tools which could be used to locate and size the DG units in the system. Particle swarm optimization (PSO) is one of the famous optimization techniques. However, the premature convergence is identified as a fundamental adverse effect of this optimization technique. Therefore, the optimization problem can direct the objective function to a local minimum. This paper presents a variant of PSO techniques, "comprehensive learning particle swarm optimization (CLPSO)"to determine the optimal placement and sizing of the DGs, which uses a novel learning strategy whereby all other particles' historical best information and learning probability value are used to update a particle's velocity. The CLPSO particles learn from one exampler for few iterations, instead of learing from global and personal best values in every iteration in PSO and this technique retains the swarm's variability to avoid premature convergence. A detailed analysis was conducted for the IEEE 33 bus system. The comparison results have revealed a higher convergence and an accuracy than the PSO. Copyright � 2020 Institute of Advanced Engineering and Science. All rights reserved.
author2 57216633155
author_facet 57216633155
Karunarathne E.
Pasupuleti J.
Ekanayake J.
Almeida D.
format Article
author Karunarathne E.
Pasupuleti J.
Ekanayake J.
Almeida D.
spellingShingle Karunarathne E.
Pasupuleti J.
Ekanayake J.
Almeida D.
Comprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reduction
author_sort Karunarathne E.
title Comprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reduction
title_short Comprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reduction
title_full Comprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reduction
title_fullStr Comprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reduction
title_full_unstemmed Comprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reduction
title_sort comprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reduction
publisher Institute of Advanced Engineering and Science
publishDate 2023
_version_ 1806428437899902976