Optimal placement and sizing of dgs in distribution networks using mlpso algorithm
Particle size analysis; Particle swarm optimization (PSO); Active power loss minimizations; Comprehensive performance; Exponential increase; Optimal placement and sizings; Optimization algorithms; Optimization techniques; Pre-mature convergences; Unity power factor; Electric power transmission netwo...
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2023
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my.uniten.dspace-250892023-05-29T16:06:44Z Optimal placement and sizing of dgs in distribution networks using mlpso algorithm Karunarathne E. Pasupuleti J. Ekanayake J. Almeida D. 57216633155 11340187300 7003409510 57211718103 Particle size analysis; Particle swarm optimization (PSO); Active power loss minimizations; Comprehensive performance; Exponential increase; Optimal placement and sizings; Optimization algorithms; Optimization techniques; Pre-mature convergences; Unity power factor; Electric power transmission networks In today�s world, distributed generation (DG) is an outstanding solution to tackle the challenges in power grids such as the power loss of the system that is intensified by the exponential increase in demand for electricity. Numerous optimization algorithms have been used by several researchers to establish the optimal placement and sizing of DGs to alleviate this power loss of the system. However, in terms of the reduction of active power loss, the performance of these algorithms is weaker. Furthermore, the premature convergence, the precision of the output, and the complexity are a few major drawbacks of these optimization techniques. Thus, this paper proposes the multileader particle swarm optimization (MLPSO) for the determination of the optimal locations and sizes of DGs with the objective of active power loss minimization while surmounting the drawbacks in previous algorithms. A comprehensive performance analysis is carried out utilizing the suggested approach on the standard IEEE 33 bus system and a real radial bus system in the Malaysian context. The findings reveal a 67.40% and an 80.32% reduction of losses in the two systems by integrating three DGs with a unity power factor, respectively. The comparison of the results with other optimization techniques demonstrated the effectiveness of the proposed MLPSO algorithm in optimal placement and sizing of DGs. � 2020 by the authors. Licensee MDPI, Basel, Switzerland. Final 2023-05-29T08:06:44Z 2023-05-29T08:06:44Z 2020 Article 10.3390/en13236185 2-s2.0-85105643419 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105643419&doi=10.3390%2fen13236185&partnerID=40&md5=e353bceb9f3d76e6b489d81e99a7306d https://irepository.uniten.edu.my/handle/123456789/25089 13 23 6185 All Open Access, Gold, Green MDPI AG Scopus |
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Malaysia |
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Universiti Tenaga Nasional |
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| description |
Particle size analysis; Particle swarm optimization (PSO); Active power loss minimizations; Comprehensive performance; Exponential increase; Optimal placement and sizings; Optimization algorithms; Optimization techniques; Pre-mature convergences; Unity power factor; Electric power transmission networks |
| author2 |
57216633155 |
| author_facet |
57216633155 Karunarathne E. Pasupuleti J. Ekanayake J. Almeida D. |
| format |
Article |
| author |
Karunarathne E. Pasupuleti J. Ekanayake J. Almeida D. |
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Karunarathne E. Pasupuleti J. Ekanayake J. Almeida D. Optimal placement and sizing of dgs in distribution networks using mlpso algorithm |
| author_sort |
Karunarathne E. |
| title |
Optimal placement and sizing of dgs in distribution networks using mlpso algorithm |
| title_short |
Optimal placement and sizing of dgs in distribution networks using mlpso algorithm |
| title_full |
Optimal placement and sizing of dgs in distribution networks using mlpso algorithm |
| title_fullStr |
Optimal placement and sizing of dgs in distribution networks using mlpso algorithm |
| title_full_unstemmed |
Optimal placement and sizing of dgs in distribution networks using mlpso algorithm |
| title_sort |
optimal placement and sizing of dgs in distribution networks using mlpso algorithm |
| publisher |
MDPI AG |
| publishDate |
2023 |
| _version_ |
1806426659892494336 |