relation: https://khub.utp.edu.my/scholars/7329/
title: Advanced Pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation
creator: Mahesh, K.
creator: Nallagownden, P.
creator: Elamvazuthi, I.
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.
publisher: MDPI AG
date: 2016
type: Article
type: PeerReviewed
identifier:   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).   ISSN 19961073     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019454015&doi=10.3390%2fen9120982&partnerID=40&md5=9b38baceaa0a96e5f720e54de8163a32
relation: 10.3390/en9120982
identifier: 10.3390/en9120982