A Genetic-Taguchi Global Design Optimization Strategy for Surface-Mounted PM Machine
| Autor: | Jian Gao, Litao Dai, Wenjuan Zhang, Shoudao Huang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
010302 applied physics
Total harmonic distortion Computer science 020208 electrical & electronic engineering Local variable 02 engineering and technology Counter-electromotive force 01 natural sciences Taguchi methods Control theory Magnet 0103 physical sciences Genetic algorithm 0202 electrical engineering electronic engineering information engineering Pole shift hypothesis Global optimization |
| Zdroj: | 2019 22nd International Conference on Electrical Machines and Systems (ICEMS). |
| DOI: | 10.1109/icems.2019.8921774 |
| Popis: | This paper presents a novel step-optimization strategy for the multi-objective tradeoffs in surface-mounted permanent magnet synchronous machines (SPMSMs), i.e. genetic-Taguchi global optimization (GTGO). Specifically, the electromagnetic performances are accurately predicted by sub-domain (SD) model, and the objective functions of cost, efficiency and total harmonic distortion (THD) of back electromotive force (emf) are solved by genetic algorithm (GA) based on constraint conditions. Next, the Taguchi method optimizes local variables which are difficult to model by SD model, such as magnetic pole shift, magnet eccentricity and segmentation. The last, the effectiveness of proposed GTGO has been verified by finite-element analysis (FEA). |
| Databáze: | OpenAIRE |
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