Application of multi-objective evolutionary optimization algorithms to reactive power planning problem

Eghbal, Mehdi, Yorino, Naoto, Zoka, Yoshifumi and El-Araby, E. E. (2009) Application of multi-objective evolutionary optimization algorithms to reactive power planning problem. IEEJ Transactions on Electrical and Electronic Engineering, 4 5: 625-632.


Author Eghbal, Mehdi
Yorino, Naoto
Zoka, Yoshifumi
El-Araby, E. E.
Title Application of multi-objective evolutionary optimization algorithms to reactive power planning problem
Journal name IEEJ Transactions on Electrical and Electronic Engineering   Check publisher's open access policy
ISSN 1931-4973
1931-4981
Publication date 2009-09
Sub-type Article (original research)
DOI 10.1002/tee.20455
Volume 4
Issue 5
Start page 625
End page 632
Total pages 8
Place of publication Hoboken, NJ, United States
Publisher John Wiley & Sons
Language eng
Abstract This paper presents a new approach to treat reactive power (VAr) planning problem using multi-objective evolutionary algorithms (EAs). Specifically, strength Pareto EA (SPEA) and multi-objective particle swarm optimization (MOPSO) approaches have been developed and successfully applied. The overall problem is formulated as a nonlinear constrained multi-objective optimization problem. Minimizing the total incurred cost of the VAr planning problem and maximizing the amount of available transfer capability (ATC) are defined as the main objective functions. The aim is to find the optimal allocation of VAr devices in such a way that investment and operating costs are minimized and at the same time the amount of ATC is maximized. The proposed approaches have been successfully tested on IEEE 14 buses system. As a result a wide set of optimal solutions known as Pareto set is obtained and encouraging results show the superiority of the proposed approaches and confirm their potential to solve such a large-scale multi-objective optimization problem.
Keyword Available transfer capability
Multi-objective evolutionary optimization
Multi-objective particle swarm optimization
Reactive power planning
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Special Issue on Intelligent Optimization in Deregulated Power Systems

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Mechanical & Mining Engineering Publications
 
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Created: Thu, 16 Dec 2010, 09:00:42 EST