Fast and Systematic Design Optimization of Surface-Mounted PM Machines Using Advanced Analytical Models and Subharmonic Elimination Methods
- Authors
- Min, Seun Guy; Sarlioglu, Bulent
- Issue Date
- Jan-2019
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Analytical solution; complex permeance (CP) model; conformal mapping; heuristic optimization; open-circuit magnetic field; Schwarz-Christoffel subdomain (SCSD) model; subdomain (SD) model; subharmonic elimination method; surface-mounted PM machines (SPMs); virtual rotor magnet
- Citation
- IEEE TRANSACTIONS ON MAGNETICS, v.55, no.1
- Journal Title
- IEEE TRANSACTIONS ON MAGNETICS
- Volume
- 55
- Number
- 1
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/39101
- DOI
- 10.1109/TMAG.2018.2877403
- ISSN
- 0018-9464
- Abstract
- This paper presents a fast design optimization method in surface-mounted permanent magnet (PM) machines using an efficient analytical model. To predict the open-circuit magnetic field, an analytical model named Schwarz-Christoffel subdomain (SCSD) is proposed by applying the virtual rotor magnets and combining two elegant models, i.e., subdomain model and complex permeance model derived from Schwarz-Christoffel conformal mapping. Based on the developed SCSD model, electromagnetic (EM) performances such as cogging torque, back-electromotive force, EM torque, unbalanced magnetic force, and machine losses can be quickly obtained by reducing the computational complexity. Subsequently, the heuristic optimization is applied for selecting the best compromise between the conflicting objectives. Since the systematic optimization methodology is proposed by isolating the optimal shape and coil design procedures, the proposed optimization model combined with SCSD is significantly fast, robust, and efficient compared with existing models. The finite-element analysis is carried out to verify analytical results, and several Pareto fronts are provided along with the comparison of the time required by each model.
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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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