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

Abstract

In 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.