eprintid: 12986
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/29/86
datestamp: 2023-11-10 03:27:33
lastmod: 2023-11-10 03:27:33
status_changed: 2023-11-10 01:50:03
type: article
metadata_visibility: show
creators_name: Truong, K.H.
creators_name: Nallagownden, P.
creators_name: Elamvazuthi, I.
creators_name: Vo, D.N.
title: An improved meta-heuristic method to maximize the penetration of distributed generation in radial distribution networks
ispublished: pub
keywords: Distributed power generation; Electric power factor; Heuristic algorithms; Optimization; Systems engineering, Optimal placements; Optimal power factors; Power loss reduction; Quasi-oppositional; Symbiotic organisms search, Heuristic methods
note: cited By 24
abstract: This paper proposes a novel scheme based on an improved meta-heuristic method to determine the optimal number of distributed generation (DG) units to be installed in distribution networks for maximum DG penetration. The proposed meta-heuristic method is the quasi-oppositional chaotic symbiotic organisms search (QOCSOS) algorithm, which is the improved version of the original SOS algorithm. QOCSOS integrates two search strategies including quasi-opposition-based learning and chaotic local search into SOS to achieve better performance. In this study, QOCSOS was implemented to find the optimal number, location, size, and power factor of DG units considering different values of DG power factor (unity and non-unity), with the objective of maximum real power loss reduction. The effectiveness of the proposed method was validated on the standard IEEE radial distribution networks including 33, 69, and 118-bus test networks. The results obtained by QOCSOS were compared to those from other methods available in the literature and the standard SOS algorithm. Comparative results revealed that QOCSOS obtained better solutions than other compared methods, and performed greater than SOS. Accordingly, QOCSOS can be a very favourable method to cope with the optimal DG placement problem. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
date: 2020
publisher: Springer
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074671305&doi=10.1007%2fs00521-019-04548-4&partnerID=40&md5=f43118813eefd2ffcb7be98be7cf8013
id_number: 10.1007/s00521-019-04548-4
full_text_status: none
publication: Neural Computing and Applications
volume: 32
number: 14
pagerange: 10159-10181
refereed: TRUE
issn: 09410643
citation:   Truong, K.H. and Nallagownden, P. and Elamvazuthi, I. and Vo, D.N.  (2020) An improved meta-heuristic method to maximize the penetration of distributed generation in radial distribution networks.  Neural Computing and Applications, 32 (14).  pp. 10159-10181.  ISSN 09410643