Multi Objective Evolutionary Algorithm Applied to the Optimal Power Flow Problem

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Data

2010-06-01

Autores

Amorim, E. A.
Hashimoto, S. H. M.
Lima, F. G. M.
Mantovani, J. R. S. [UNESP]

Título da Revista

ISSN da Revista

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Editor

Institute of Electrical and Electronics Engineers (IEEE)

Resumo

This work presents the application of a multiobjective evolutionary algorithm (MOEA) for optimal power flow (OPF) solution. The OPF is modeled as a constrained nonlinear optimization problem, non-convex of large-scale, with continuous and discrete variables. The violated inequality constraints are treated as objective function of the problem. This strategy allows attending the physical and operational restrictions without compromise the quality of the found solutions. The developed MOEA is based on the theory of Pareto and employs a diversity-preserving mechanism to overcome the premature convergence of algorithm and local optimal solutions. Fuzzy set theory is employed to extract the best compromises of the Pareto set. Results for the IEEE-30, RTS-96 and IEEE-354 test systems are presents to validate the efficiency of proposed model and solution technique.

Descrição

Palavras-chave

Multiobjective Evolutionary Algorithm, Optimal Power Flow, Multiobjective Optimization

Como citar

IEEE Latin America Transactions. Piscataway: IEEE-Inst Electrical Electronics Engineers Inc, v. 8, n. 3, p. 236-244, 2010.