Hybrid constrained genetic algorithm/particle swarm optimisation load flow algorithm

T. O. Ting; K. P. Wong; C. Y. Chung

doi:10.1049/iet-gtd:20070224

Hybrid constrained genetic algorithm/particle swarm optimisation load flow algorithm

T. O. Ting, K. P. Wong, C. Y. Chung

Research output: Journal article publication › Journal article › Academic research › peer-review

30 Citations (Scopus)

Abstract

A hybrid constrained genetic algorithm and particle swarm optimisation (PSO) method for the evaluation of the load flow in heavy-loaded power systems is developed. The new algorithm is applied to find the maximum loading points of three IEEE test systems. The experimental determination of the best values of the parameters for use in the PSO part of the hybrid algorithm is also reported.

Original language	English
Pages (from-to)	800-812
Number of pages	13
Journal	IET Generation, Transmission and Distribution
Volume	2
Issue number	6
DOIs	https://doi.org/10.1049/iet-gtd:20070224
Publication status	Published - Nov 2008

ASJC Scopus subject areas

Control and Systems Engineering
Energy Engineering and Power Technology
Electrical and Electronic Engineering

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10.1049/iet-gtd:20070224

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abstract = "A hybrid constrained genetic algorithm and particle swarm optimisation (PSO) method for the evaluation of the load flow in heavy-loaded power systems is developed. The new algorithm is applied to find the maximum loading points of three IEEE test systems. The experimental determination of the best values of the parameters for use in the PSO part of the hybrid algorithm is also reported.",

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Hybrid constrained genetic algorithm/particle swarm optimisation load flow algorithm

Abstract

ASJC Scopus subject areas

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