A 33kV distribution network feeder reconfiguration by Using REPSO for voltage profile improvement

Sulaima, Mohamad Fani and Othman, Siti Noratika and Jali, Mohd Hafiz and Jamri , Mohd Saifuzam and Mohd Nasir, Mohamad Na'im and Bohari, Zul Hasrizal (2014) A 33kV distribution network feeder reconfiguration by Using REPSO for voltage profile improvement. International Journal of Applied Engineering Research, 9 (18). pp. 4569-4582. ISSN 0973-4562

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Abstract

The complexity of modern power system has contributed to the high power losses and over load in the distribution network. Due to that reason, Feeder Reconfiguration (FR) is required to identify the best topology network in order to fulfill the power demand with reduced power losses while stabilizing the magnitude of voltage. This paper addresses a new optimization method which is called as Rank Evolutionary Particle Swarm Optimization (REPSO). It has been produced by a hybridization of the conventional Particle Swarm Optimization (PSO) and the traditional Evolutionary Programming (EP) algorithm. The main objective of this paper is to improve the voltage profile while solves the overload problem by reducing the power losses respectively. The proposed method has been implemented and the real power losses in the 33kVdistribution system has been investigated and analyzed accordingly. The results are compared to the conventional Genetic Algorithm (GA), EP and PSO techniques and it is hoped to help the power system engineer in securing the network in the future.

Item Type:	Article
Uncontrolled Keywords:	Feeder reconfiguration, Rank evolutionary particle swarm optimization, Particle swarm optimization, Evolutionary programming, Genetic algorithm, Voltage profile, Power losses
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Electrical Engineering > Department of Industrial Power
Depositing User:	MOHAMAD FANI SULAIMA
Date Deposited:	15 Jul 2014 08:04
Last Modified:	24 Jul 2023 15:52
URI:	http://eprints.utem.edu.my/id/eprint/12889
Statistic Details:	View Download Statistic

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