Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.409780
Title: Design & optimisation of the flux switching motor and drive with genetic algorithms
Author: Chai, Kao Siang
ISNI:       0000 0001 3525 9301
Awarding Body: University of Leicester
Current Institution: University of Leicester
Date of Award: 2004
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Abstract:
The Flux switching motor is a new class of reluctance machine that has demonstrated potential as a possible replacement for brushed-dc motor in many applications. However the design and optimisation of the motor and its drive system are rather complicated and not much past knowledge and guidelines are available to aid the engineer(s) in the design of the machine.;The development of flexible and versatile design optimisation software to facilitate the design and optimisation of FS motor and drive is presented. The design optimisation software incorporates a genetic algorithm optimisation tool and dynamic simulation model with third party finite element analysis software.;The developed genetic algorithm optimisation program integrated with finite element analysis software provides the engineer with the necessary optimisation tools capable of interfacing with the FEA software. This has allowed many FSM lamination designs to be created without any requirement of user feedback once the program is initialised. In addition the application of the developed design tool can also be extended to other electromagnetic devices.;A dynamic simulation model of the FS motor drive system has been developed. The model can either be used as a standalone program or be integrated into the optimisation software. The dynamic simulation model consisted of a simple time-stepping electrical equivalent circuit coupled with a switch control algorithm, a winding optimisation model and an iron loss model. When interfaced with the FEA software it can support rapid estimation of the motor dynamic performance. The developed optimisation software has been used to design and optimise FS motors and the results have demonstrated the potential of genetic algorithms in design optimisation of the machine.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.409780  DOI: Not available
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