Application of differential evolution to power system stabilizer design

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dc.contributor.advisorFolly, Komla Aen_ZA
dc.contributor.authorMulumba, Tshina Faen_ZA
dc.date.accessioned2015-01-11T04:42:40Z
dc.date.available2015-01-11T04:42:40Z
dc.date.issued2012en_ZA
dc.descriptionIncludes synopsis.en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractIn recent years, many Evolutionary Algorithms (EAs) such as Genetic Algorithms (GAs) have been proposed to optimally tune the parameters of the PSS. GAs are population based search methods inspired by the mechanism of evolution and natural genetic. Despite the fact that GAs are robust and have given promising results in many applications, they still have some drawbacks. Some of these drawbacks are related to the problem of genetic drift in GA which restricts the diversity in the population. ... To cope with the above mentioned drawbacks, many variants of GAs have been proposed often tailored to a particular problem. Recently, several simpler and yet effective heuristic algorithms such as Population Based Incremental Learning (PBIL) and Differential Evolution (DE), etc., have received increasing attention.en_ZA
dc.identifier.apacitationMulumba, T. F. (2012). <i>Application of differential evolution to power system stabilizer design</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/12026en_ZA
dc.identifier.chicagocitationMulumba, Tshina Fa. <i>"Application of differential evolution to power system stabilizer design."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2012. http://hdl.handle.net/11427/12026en_ZA
dc.identifier.citationMulumba, T. 2012. Application of differential evolution to power system stabilizer design. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Mulumba, Tshina Fa AB - In recent years, many Evolutionary Algorithms (EAs) such as Genetic Algorithms (GAs) have been proposed to optimally tune the parameters of the PSS. GAs are population based search methods inspired by the mechanism of evolution and natural genetic. Despite the fact that GAs are robust and have given promising results in many applications, they still have some drawbacks. Some of these drawbacks are related to the problem of genetic drift in GA which restricts the diversity in the population. ... To cope with the above mentioned drawbacks, many variants of GAs have been proposed often tailored to a particular problem. Recently, several simpler and yet effective heuristic algorithms such as Population Based Incremental Learning (PBIL) and Differential Evolution (DE), etc., have received increasing attention. DA - 2012 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2012 T1 - Application of differential evolution to power system stabilizer design TI - Application of differential evolution to power system stabilizer design UR - http://hdl.handle.net/11427/12026 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/12026
dc.identifier.vancouvercitationMulumba TF. Application of differential evolution to power system stabilizer design. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2012 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/12026en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherElectrical Engineeringen_ZA
dc.titleApplication of differential evolution to power system stabilizer designen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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