Design of power system stabilizer: a comparison between genetic algorithms (GAs) and population-based incremental learning (PBIL)

Author

Folly, K.A.

Author_Institution

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

fYear

0

fDate

0-0 0

Abstract

This paper compares a method of designing power system stabilizer for a multimachine power system using a simple evolutionary algorithm called population-based incremental learning (PBIL) and genetic algorithms (GAs). The controller design issue is formulated as an optimization problem that is solved via PBIL algorithm and GAs. The resulting controllers are tested on both the nominal and off-nominal operating conditions. Simulation results show that PBIL-PSSs perform comparably to GA-PSSs

Keywords

control system synthesis; genetic algorithms; power system control; power system stability; controller design; evolutionary algorithm; genetic algorithms; multimachine power system; population-based incremental learning; power system stabilizer design; Algorithm design and analysis; Cities and towns; Genetic algorithms; Genetic mutations; Power system analysis computing; Power system interconnection; Power system modeling; Power system simulation; Power system stability; Power systems; Genetic algorithms; low-frequency oscillations; population-based incremental learning; power system stabilizer;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Engineering Society General Meeting, 2006. IEEE

Conference_Location

Montreal, Que.

Print_ISBN

1-4244-0493-2

Type

conf

DOI

10.1109/PES.2006.1709635

Filename

1709635