Radial distribution network reconfiguration for power losses reduction using a modified particle swarm optimisation


Abstract: Recently, losses reduction gained a great deal of attention in distribution system due to low-voltage level and the high-current passing through the lines, pushing the distribution utilities to improve their profit margins on one hand by reducing the unnecessary operational cost, and improving their delivered power quality on the other hand by maintaining the system reliability, and the continuity of supply for varying load demand. Load balancing, voltage regulation, network reconfiguration and others are different techniques used to reduce the losses. This study addresses the distribution network reconfiguration to minimise the network losses. A new modified form of particle swarm optimisation (PSO) is used to identify the optimal configuration of distribution network effectively. The difference between the modified PSO (MPSO) algorithms and the typical one is the filtered random selective search space for initial position, which is proposed to accelerate the algorithm for reaching the optimum solution. The suggested MPSO is tested via 33 and 69 IEEE networks. A benchmark comparison has been conducted to prove the effectiveness of MPSO compared with previous optimisation techniques.

Publication DOI: https://doi.org/10.1049/oap-cired.2017.1286
Divisions: College of Engineering & Physical Sciences > Power Electronics, Machines and Power System (PEMPS)
Additional Information: This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/).
Uncontrolled Keywords: voltage control,power supply quality,profitability,search problems, power distribution economics,minimisation,power distribution reliability,particle swarm optimisation
Publication ISSN: 2047-4962
Last Modified: 16 May 2024 07:12
Date Deposited: 22 Dec 2017 09:40
Full Text Link: http://digital- ... cired.2017.1286
Related URLs:
PURE Output Type: Article
Published Date: 2017-10-01
Published Online Date: 2017-10-01
Accepted Date: 2017-10-01
Authors: Atteya, Inji
Ashour, H.A.
Fahmi, Nagi R
Strickland, Danielle R



Version: Published Version

License: Creative Commons Attribution

| Preview

Export / Share Citation


Additional statistics for this record