Genetic algorithm based approach of SRM current profiling for torque control and minimal copper losses
MacRae, Euan and Abdel-Aziz, Ali and Ahmed, Khaled and Pollock, Richard and Williams, Barry W.; (2023) Genetic algorithm based approach of SRM current profiling for torque control and minimal copper losses. In: 2023 IEEE International Electric Machines & Drives Conference (IEMDC). IEEE, USA. ISBN 9798350398991 (https://doi.org/10.1109/IEMDC55163.2023.10239015)
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Abstract
This paper presents a novel approach to current profiling for switched reluctance machines that eliminates torque ripple while inherently guaranteeing minimum copper losses, along with linear torque control. Minimization of copper losses increases machine efficiency, while eliminating torque ripple is the pre requisite for SRM use in applications such as traction vehicles. This paper presents theoretical optimal current profiles, initially without consideration of DC link voltage limitations. Utilizing a Genetic Algorithm in conjunction with current profiling limit envelopes, an optimized set of current profiles across the torque ripple free speed range of an exemplary 8/6 SRM is then created. The profiles characteristics are analyzed and compared with commonly used torque sharing function control to confirm the merits of the proposed method.
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Item type: Book Section ID code: 87700 Dates: DateEvent6 September 2023Published20 February 2023AcceptedNotes: Copyright © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Subjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Strategic Research Themes > EnergyDepositing user: Pure Administrator Date deposited: 20 Dec 2023 16:32 Last modified: 20 Nov 2024 01:35 URI: https://strathprints.strath.ac.uk/id/eprint/87700