Advanced Pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation

This paper proposes an advanced Pareto-front non-dominated sorting multi-objective particle swarm optimization (Advanced-PFNDMOPSO) method for optimal configuration (placement and sizing) of distributed generation (DG) in the radial distribution system. The distributed generation consists of single...

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Main Authors: Mahesh, K., Nallagownden, P., Elamvazuthi, I.
Format: Article
Published: MDPI AG 2016
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019454015&doi=10.3390%2fen9120982&partnerID=40&md5=9b38baceaa0a96e5f720e54de8163a32
http://eprints.utp.edu.my/25904/
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spelling my.utp.eprints.259042021-08-27T13:09:54Z Advanced Pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation Mahesh, K. Nallagownden, P. Elamvazuthi, I. This paper proposes an advanced Pareto-front non-dominated sorting multi-objective particle swarm optimization (Advanced-PFNDMOPSO) method for optimal configuration (placement and sizing) of distributed generation (DG) in the radial distribution system. The distributed generation consists of single and multiple numbers of active power DG, reactive power DG and simultaneous placement of active-reactive power DG. The optimization problem considers two multi-objective functions, i.e., power loss reduction and voltage stability improvements with voltage profile and power balance as constraints. First, the numerical output results of objective functions are obtained in the Pareto-optimal set. Later, fuzzy decision model is engendered for final selection of the compromised solution. The proposed method is employed and tested on standard IEEE 33 bus systems. Moreover, the results of proposed method are validated with other optimization algorithms as reported by others in the literature. The overall outcome shows that the proposed method for optimal placement and sizing gives higher capability and effectiveness to the final solution. The study also reveals that simultaneous placement of active-reactive power DG reduces more power losses, increases voltage stability and voltage profile of the system. © 2016 by the authors; licensee MDPI. MDPI AG 2016 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019454015&doi=10.3390%2fen9120982&partnerID=40&md5=9b38baceaa0a96e5f720e54de8163a32 Mahesh, K. and Nallagownden, P. and Elamvazuthi, I. (2016) Advanced Pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation. Energies, 9 (12). http://eprints.utp.edu.my/25904/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description This paper proposes an advanced Pareto-front non-dominated sorting multi-objective particle swarm optimization (Advanced-PFNDMOPSO) method for optimal configuration (placement and sizing) of distributed generation (DG) in the radial distribution system. The distributed generation consists of single and multiple numbers of active power DG, reactive power DG and simultaneous placement of active-reactive power DG. The optimization problem considers two multi-objective functions, i.e., power loss reduction and voltage stability improvements with voltage profile and power balance as constraints. First, the numerical output results of objective functions are obtained in the Pareto-optimal set. Later, fuzzy decision model is engendered for final selection of the compromised solution. The proposed method is employed and tested on standard IEEE 33 bus systems. Moreover, the results of proposed method are validated with other optimization algorithms as reported by others in the literature. The overall outcome shows that the proposed method for optimal placement and sizing gives higher capability and effectiveness to the final solution. The study also reveals that simultaneous placement of active-reactive power DG reduces more power losses, increases voltage stability and voltage profile of the system. © 2016 by the authors; licensee MDPI.
format Article
author Mahesh, K.
Nallagownden, P.
Elamvazuthi, I.
spellingShingle Mahesh, K.
Nallagownden, P.
Elamvazuthi, I.
Advanced Pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation
author_facet Mahesh, K.
Nallagownden, P.
Elamvazuthi, I.
author_sort Mahesh, K.
title Advanced Pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation
title_short Advanced Pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation
title_full Advanced Pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation
title_fullStr Advanced Pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation
title_full_unstemmed Advanced Pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation
title_sort advanced pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation
publisher MDPI AG
publishDate 2016
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019454015&doi=10.3390%2fen9120982&partnerID=40&md5=9b38baceaa0a96e5f720e54de8163a32
http://eprints.utp.edu.my/25904/
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score 13.18916