NDLI: Genetic algorithms based dynamic search spaces for global power system stabilizer optimization

Content Provider	IEEE Xplore Digital Library
Author	Alkhatib, H. Duveau, J. Pasquinelli, M.
Copyright Year	2009
Description	Author affiliation: Institut Matériaux Microélectronique Nanosciences de Provence, Université Paul Cézanne (Aix-Marseille III), France (Pasquinelli, M.) \|\| Laboratoire de Compatibilité Electromagnétique de Marseille, Université Paul Cézanne (Aix-Marseille III), France (Alkhatib, H.; Duveau, J.)
Abstract	Genetic algorithms (GAs) are powerful optimization techniques. The optimization performance depends highly on the determination of optimized parameter search spaces, which remain unchanged during GA running. Hence, the objective function evolution may decelerate or even stabilize well before attaining the optimal solution. This article proposes an approach of GAs based dynamic search spaces. It focuses on improving the search space boundaries and allowing GAs to discover new search spaces which are not accessible initially. A GA using this approach is developed and validated to the optimization of power system stabilizer parameters within a multimachine system (16-generator and 68-bus). The obtained results are evaluated and compared with those of ordinary GAs and literature. They show significant improvement in terms of optimization performance and convergence rate.
Starting Page	1084
Ending Page	1089
File Size	325002
Page Count	6
File Format	PDF
ISBN	9781424446841
DOI	10.1109/MED.2009.5164690
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-24
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Damping Power system transients Power system dynamics Power system interconnection Optimization methods Power system stability Automatic control Power systems Power system modeling Genetic algorithms
Content Type	Text
Resource Type	Article

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