An enhanced RCGA for a rapid and reliable load flow solution of electrical power systems

Kubba, H. and Mokhlis, Hazlie (2012) An enhanced RCGA for a rapid and reliable load flow solution of electrical power systems. International Journal of Electrical Power & Energy Systems, 43 (1). pp. 304-312. ISSN 0142-0615, DOI https://doi.org/10.1016/j.ijepes.2012.04.034.

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Official URL: http://ac.els-cdn.com/S0142061512001615/1-s2.0-S01...

Abstract

The paper presents a reliable and fast load flow solution by using a real-coded genetic algorithm (RCGA), bus reduction technique and sparsity technique. The proposed load flow solution firstly used reduction technique to eliminate the load buses. Then, the power flow problem is solved for the generator buses only using real-coded GA to calculate the phase angles. Thus, the load flow problem becomes a single objective function, where the voltage magnitudes are specified resulted in reduced computation time for the solution. Once the phase angle has been calculated, the system is restored by calculating the voltages of the load buses in terms of the calculated voltages of the generator buses. A sparsity technique is used to reduce the computation time further as well as the storage requirements. The proposed load flow solution also can efficiently solve the load flow problems for ill-conditioned power systems whereas the conventional RCGA alone fails to solve these systems. The proposed method was demonstrated on 14-bus IEEE, 30-bus IEEE and 300-bus IEEE, and a practical system 362-busbar Iraqi National Grid. The proposed solution has reliable convergence, a highly accurate solution and much less computing time for on-line applications. The method can conveniently be applied for on-line analysis and planning studies of large power systems. (C) 2012 Elsevier Ltd. All rights reserved.

Item Type: Article
Funders: UNSPECIFIED
Additional Information: 038OO Times Cited:1 Cited References Count:24
Uncontrolled Keywords: Genetic algorithms; Load flow analysis; Load modeling; Modeling; Sparse matrices; Simulation
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 11 Jul 2013 03:15
Last Modified: 09 Oct 2019 09:22
URI: http://eprints.um.edu.my/id/eprint/7821

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