Weight optimisation of a salient pole synchronous generator by a new genetic algorithm validated by finite element analysis

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dc.contributor.authorÇelebi M.
dc.date.accessioned2024-07-22T08:21:37Z
dc.date.available2024-07-22T08:21:37Z
dc.date.issued2009
dc.description.abstractIn this study, a new approach for genetic algorithm (GA) is proposed and compared with conventional GA (CGA) in the weight optimisation of a 2-MVA salient pole synchronous machine. The main differences between the two algorithms are that, in the newly proposed method, individuals are paired and crossed over based on the Mendelian rules of genetics, and the mutation operator is omitted. The rules concern the segregation of Alleles and the independent assortment of Alleles. This approach is comprehensive and conceptually accurate since its framework uses Mendelian population genetics. The operation CPU time is longer in the new approach when compared to the conventional one but can be ignored in electric machine design since it is not a real-time process. The results of the analytic solution and the new and CGA implementation methods are compared in terms of weight, efficiency and temperature. The results obtained are similar to those of the conventional ones and even better in some cases. A finite element analysis (FEA) is done to realise the machine designs optimised by the new GA (NGA) and CGA for the case of a fixed 24-pole design. Hence the improvement over CGA achieved by NGA has been validated through FEA. © 2009 The Institution of Engineering and Technology.
dc.identifier.DOI-ID10.1049/iet-epa.2008.0126
dc.identifier.issn17518679
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/18685
dc.language.isoEnglish
dc.subjectBiology
dc.subjectChromosomes
dc.subjectElectric machinery
dc.subjectFinite element method
dc.subjectGenetic algorithms
dc.subjectPoles
dc.subjectSynchronous generators
dc.subjectAnalytic solution
dc.subjectCPU time
dc.subjectElectric machine design
dc.subjectFinite element analysis
dc.subjectImplementation methods
dc.subjectMutation operators
dc.subjectNew approaches
dc.subjectOptimisation
dc.subjectPopulation genetics
dc.subjectReal-time process
dc.subjectSalient pole synchronous generator
dc.subjectSalient pole synchronous machines
dc.subjectMachine design
dc.titleWeight optimisation of a salient pole synchronous generator by a new genetic algorithm validated by finite element analysis
dc.typeArticle

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