Abstract
This paper presents an analytical method to design an optimum Line-Start Permanent-Magnet Synchronous Motor (LSPMSM). An initial LSPMSM (L0) is designed by combining an induction motor (IM0) and an interior permanent magnet (IPM0) motor. The IM0 and IPM0 are designed analytically from sizing equations. The optimum LSPMSM is developed from IM cage optimization for starting torque and permanent magnet (PM) optimization of the IPM for efficiency. By combining the rotors of the optimised IM and IPM, an optimum hybrid rotor is obtained for the LSPMSM. The optimized motor has better performance in the transient and steady state compared to L0. The proposed optimisation method based on a mathematical model is implemented using a genetic algorithm (GA). An optimization case study is implemented using both the proposed mathematical method and FEM. The optimization results indicate that the optimum design found using the proposed mathematical method is obtained more rapidly and has comparable performance to that found using FEM optimization.
Original language | English |
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Title of host publication | ECCE 2020 - IEEE Energy Conversion Congress and Exposition |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 2064-2071 |
Number of pages | 8 |
ISBN (Electronic) | 9781728158266, 9781728158273 |
DOIs | |
Publication status | Published - 11 Oct 2020 |
Event | 12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020 - Virtual, Detroit, United States Duration: 11 Oct 2020 → 15 Oct 2020 Conference number: 12 |
Publication series
Name | ECCE 2020 - IEEE Energy Conversion Congress and Exposition |
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Conference
Conference | 12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020 |
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Country/Territory | United States |
City | Virtual, Detroit |
Period | 11/10/20 → 15/10/20 |
Keywords
- FEM
- Genetic Algorithms
- Induction Motor
- Interior Permanent-Magnet Motor
- Line-Start Permanent-Magnet Synchronous Motor
- Mathematical Model
- Optimization
- Sizing Equations
- Steady-state
- Transient