| Author: | Qu, Guangyu |
| Title: | Design of permanent magnet motor and its related genetic optimization algorithm |
| Advisors: | Fu, W. N. (EE) |
| Degree: | M.Sc. |
| Year: | 2015 |
| Subject: | Permanent magnet motors -- Design. Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Electrical Engineering |
| Pages: | v, 95 pages : color illustrations |
| Language: | English |
| Abstract: | Recent years, the permanent magnet motor has become much more popular used than any other types of motors. Thus, the best performance of a specific motor can be simulated by combining C with Maxwell. Usually, it's hard to get motors' best performance by several changing variables. Because I need create a material object to measure the parameters at first, then I modify the material object to get a better performance. Unfortunately, it would cost a lot of money, thus it's unsuitable. However, Ansoft Maxwell can cover this problem. With its powerful simulation function, it can simulate a great number of motors with different parameters. And C++ language can help the process of simulating to work automatically, which would save the time and improve the efficiency greatly. To get the best specified motor parameters, we usually choose to use the genetic algorithm to help Ansoft Maxwell to simulate the specified optimize outcome. The specific ideas is that use the Ansoft Maxwell to simulate the model of motor circularly, then use genetic algorithm to control the changing variables of Ansoft Maxwell, and finally the whole process is controlled by the C++ language. The permanent magnet motor has the advantages like high efficiency, simple structure, small volume, light weight loss, reliable operation, and so on. Therefore, developing the permanent magnet excitation motor is bound to become the mainstream development direction in the future. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b28254090.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.7 MB | Adobe PDF | View/Open |
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