Multimachine power system stabilizer design based on a simplified version of genetic algorithms combined with learning

Author

Folly, K.A.

Author_Institution

Dept. of Electr. Eng., Cape Town Univ.

fYear

2005

fDate

6-10 Nov. 2005

Abstract

A method of designing power system stabilizer for a multimachine power system using a simplified version of genetic algorithms (GAs) called population-based incremental learning (PBIL) is proposed in this paper. The controller design problem is converted into an optimization problem that is solved via the PBIL algorithm. The resulting controllers ensure robust stability and good performance for both the nominal and off-nominal operating conditions. Simulation results are presented to show the effectiveness of PBIL-PSSs

Keywords

control system synthesis; genetic algorithms; learning (artificial intelligence); power system control; power system stability; controller design problem; genetic algorithm; low-frequency oscillation; multimachine power system stabilizer design; population-based incremental learning; robust stability; Algorithm design and analysis; Control systems; Genetic algorithms; Power system analysis computing; Power system control; Power system simulation; Power system stability; Power systems; Robust control; Robust stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Application to Power Systems, 2005. Proceedings of the 13th International Conference on

Conference_Location

Arlington, VA

Print_ISBN

1-59975-174-7

Type

conf

DOI

10.1109/ISAP.2005.1599270

Filename

1599270