Breeder Genetic Algorithm for Power System Stabilizer design

This paper presents the design of Power System Stabilizers (PSSs) using two Evolutionary Algorithm (EA) techniques, namely; Genetic Algorithm (GA) and Breeder Genetic Algorithm. BGA is a new form of evolutionary algorithm, which is based on the idea of survival of the fittest, but differs from the t...

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Bibliographic Details
Main Authors: Sheetekela, Severus, Folly, Komla Agbenyo
Format: Conference Proceeding
Language:English
Subjects:
Damping
Eigenvalues and eigenfunctions
Evolutionary computation
Genetic algorithms
Genetics
Power system stability
Online Access:Request full text
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Summary:This paper presents the design of Power System Stabilizers (PSSs) using two Evolutionary Algorithm (EA) techniques, namely; Genetic Algorithm (GA) and Breeder Genetic Algorithm. BGA is a new form of evolutionary algorithm, which is based on the idea of survival of the fittest, but differs from the traditional Genetic Algorithm due to its artificial breeding nature. An eigenvalue based objective function is used in the design of the PSSs whereby the algorithms maximize the lowest damping ratio over specified operating conditions. For comparison purpose, the Conventional PSS (CPSS) is also included. The performance and effectiveness of the PSSs in damping the electromechanical modes is investigated. Eigenvalue analysis and time domain simulations show that BGA-PSS and GA-PSS perform better than the CPSS for all the operating conditions considered except at the nominal operating condition. However, BGA-PSS performs slightly better than the GA-PSS.
ISSN:1089-778X
1941-0026
DOI:10.1109/CEC.2010.5586397