Solving multi-contingency transient stability constrained optimal power flow problems with an improved GA

K. Y. Chan; S. H. Ling; Ka Wing Chan; H. H C Iu; G. T Y Pong

doi:10.1109/CEC.2007.4424840

Solving multi-contingency transient stability constrained optimal power flow problems with an improved GA

K. Y. Chan, S. H. Ling, Ka Wing Chan, H. H C Iu, G. T Y Pong

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

20 Citations (Scopus)

Abstract

In this paper, an improved genetic algorithm has been proposed for solving multi-contingency transient stability constrained optimal power flow (MC-TSCOPF) problems. The MC-TSCOPF problem is formulated as an extended optimal power flow (OPF) with additional generator rotor angle constraints and is converted into an unconstrained optimization problem, which is suitable for genetic algorithms to deal with, using a penalty function. The improved genetic algorithm is proposed by incorporating an orthogonal design in exploring solution spaces. A case study indicates that the improved genetic algorithm outperforms the existing genetic algorithm-based method in terms of robustness of solutions and the convergence speed while the solution quality can be kept.

Original language	English
Title of host publication	2007 IEEE Congress on Evolutionary Computation, CEC 2007
Pages	2901-2908
Number of pages	8
DOIs	https://doi.org/10.1109/CEC.2007.4424840
Publication status	Published - 1 Dec 2007
Event	2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore Duration: 25 Sep 2007 → 28 Sep 2007

Conference

Conference	2007 IEEE Congress on Evolutionary Computation, CEC 2007
Country	Singapore
Period	25/09/07 → 28/09/07

ASJC Scopus subject areas

Artificial Intelligence
Software
Theoretical Computer Science

More information

10.1109/CEC.2007.4424840

Cite this

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abstract = "In this paper, an improved genetic algorithm has been proposed for solving multi-contingency transient stability constrained optimal power flow (MC-TSCOPF) problems. The MC-TSCOPF problem is formulated as an extended optimal power flow (OPF) with additional generator rotor angle constraints and is converted into an unconstrained optimization problem, which is suitable for genetic algorithms to deal with, using a penalty function. The improved genetic algorithm is proposed by incorporating an orthogonal design in exploring solution spaces. A case study indicates that the improved genetic algorithm outperforms the existing genetic algorithm-based method in terms of robustness of solutions and the convergence speed while the solution quality can be kept.",

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Solving multi-contingency transient stability constrained optimal power flow problems with an improved GA. / Chan, K. Y.; Ling, S. H.; Chan, Ka Wing; Iu, H. H C; Pong, G. T Y.

2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. p. 2901-2908 4424840.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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Solving multi-contingency transient stability constrained optimal power flow problems with an improved GA

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