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This paper looks at a novel optimisation approach to the design of surface mounted permanent magnet (SMPM) machines with self-sensing capabilities. A methodology will be presented which will look at the use of genetic algorithms (GA) to contemporarily maximise the output torque and the self sensing properties of such machines. A GA optimisation environment has been grafted with a finite element analysis (FEA) environment to enable the designer to account for both geometrical and saturation saliencies for an effective determination of the machine's self sensing characteristics. Satisfactory results were obtained in terms of torque maximization and self sensing capability. In addition sensitivity of the major geometrical parameters of the machine will be discussed in terms torque density and the self-sensing.

Synchronous reluctance motors (SynRMs) are an alternative solution in low-cost applications due to some advantages in terms of manufacturing simplicity. This study deals with a new design and implementation of a SynRM so as to operate at low-voltage level produced by solar panels without using any boost-converter. A 4-inch of standard frame size is chosen for as a submersible pump motor. Preparing for the optimisation step, the commercial design software, Infolytica is used to create the first base structure of the SynRM. As the first step the dc-link voltage, rated current, rated speed, winding layout, slots number, number of the rotor barriers and the outer/inner diameters of the stator are determined. In the next step, the base structure is optimised by using a genetic algorithm (GA) developed in Matlab environment. The developed GA script and finite element analysis software are operated together for optimisation of the rotor geometry. A direct torque control algorithm is used to analyse the performance of the designed motor. The designed motor is also manufactured and tested experimentally.

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(2015) Permanent magnet machine design trade-offs to achieve sensorless control at high load. COMPEL -The international journal for computation and mathematics in electrical and electronic engineering, 34 (1 A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk