Robust multiobjective and multidisciplinary design optimization of electrical drive systems
Design and optimization of electrical drive systems often involve simultaneous consideration of multiple objectives that usually contradict to each other and multiple disciplines that normally coupled to each other. This paper aims to present efficient system-level multiobjective optimization method...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
China Electrotechnical Society
2018-12-01
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| Series: | CES Transactions on Electrical Machines and Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8598260 |
| _version_ | 1797768046609170432 |
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| author | Gang Lei Tianshi Wang Jianguo Zhu Youguang Guo |
| author_facet | Gang Lei Tianshi Wang Jianguo Zhu Youguang Guo |
| author_sort | Gang Lei |
| collection | DOAJ |
| description | Design and optimization of electrical drive systems often involve simultaneous consideration of multiple objectives that usually contradict to each other and multiple disciplines that normally coupled to each other. This paper aims to present efficient system-level multiobjective optimization methods for the multidisciplinary design optimization of electrical drive systems. From the perspective of quality control, deterministic and robust approaches will be investigated for the development of the optimization models for the proposed methods. Meanwhile, two approximation methods, Kriging model and Taylor expansion are employed to decrease the computation/simulation cost. To illustrate the advantages of the proposed methods, a drive system with a permanent magnet synchronous motor driven by a field oriented control system is investigated. Deterministic and robust Pareto optimal solutions are presented and compared in terms of several steady-state and dynamic performances (like average torque and speed overshoot) of the drive system. The robust multiobjective optimization method can produce optimal Pareto solutions with high manufacturing quality for the drive system. |
| first_indexed | 2024-03-12T20:48:50Z |
| format | Article |
| id | doaj.art-16f4af40aca144d5861af94e9e6b8a03 |
| institution | Directory Open Access Journal |
| issn | 2096-3564 2837-0325 |
| language | English |
| last_indexed | 2024-03-12T20:48:50Z |
| publishDate | 2018-12-01 |
| publisher | China Electrotechnical Society |
| record_format | Article |
| series | CES Transactions on Electrical Machines and Systems |
| spelling | doaj.art-16f4af40aca144d5861af94e9e6b8a032023-08-01T07:13:46ZengChina Electrotechnical SocietyCES Transactions on Electrical Machines and Systems2096-35642837-03252018-12-012440941610.30941/CESTEMS.2018.00052Robust multiobjective and multidisciplinary design optimization of electrical drive systemsGang Lei0Tianshi Wang1Jianguo Zhu2Youguang Guo3University of Technology Sydney, Sydney, NSW, AUUniversity of Technology Sydney, Sydney, NSW, AUThe University of Sydney, Sydney, NSW, AUUniversity of Technology Sydney, Sydney, NSW, AUDesign and optimization of electrical drive systems often involve simultaneous consideration of multiple objectives that usually contradict to each other and multiple disciplines that normally coupled to each other. This paper aims to present efficient system-level multiobjective optimization methods for the multidisciplinary design optimization of electrical drive systems. From the perspective of quality control, deterministic and robust approaches will be investigated for the development of the optimization models for the proposed methods. Meanwhile, two approximation methods, Kriging model and Taylor expansion are employed to decrease the computation/simulation cost. To illustrate the advantages of the proposed methods, a drive system with a permanent magnet synchronous motor driven by a field oriented control system is investigated. Deterministic and robust Pareto optimal solutions are presented and compared in terms of several steady-state and dynamic performances (like average torque and speed overshoot) of the drive system. The robust multiobjective optimization method can produce optimal Pareto solutions with high manufacturing quality for the drive system.https://ieeexplore.ieee.org/document/8598260electrical drive systemselectrical machinesmultidisciplinary design optimizationmultiobjective optimizationrobust design optimization |
| spellingShingle | Gang Lei Tianshi Wang Jianguo Zhu Youguang Guo Robust multiobjective and multidisciplinary design optimization of electrical drive systems CES Transactions on Electrical Machines and Systems electrical drive systems electrical machines multidisciplinary design optimization multiobjective optimization robust design optimization |
| title | Robust multiobjective and multidisciplinary design optimization of electrical drive systems |
| title_full | Robust multiobjective and multidisciplinary design optimization of electrical drive systems |
| title_fullStr | Robust multiobjective and multidisciplinary design optimization of electrical drive systems |
| title_full_unstemmed | Robust multiobjective and multidisciplinary design optimization of electrical drive systems |
| title_short | Robust multiobjective and multidisciplinary design optimization of electrical drive systems |
| title_sort | robust multiobjective and multidisciplinary design optimization of electrical drive systems |
| topic | electrical drive systems electrical machines multidisciplinary design optimization multiobjective optimization robust design optimization |
| url | https://ieeexplore.ieee.org/document/8598260 |
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