Analysis and Optimization of Axial Flux Permanent Magnet Machine for Cogging Torque Reduction
| Autor: | Syed Sabir Hussain Bukhari, Jong-Suk Ro, Khurram Saleem Alimgeer, Hina Usman, Muhammad Yousuf, Junaid Ikram |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Materials science
General Mathematics PM overhang 02 engineering and technology 01 natural sciences 3D FEA Control theory 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) QA1-939 Torque hexagonal-shaped PMs Engineering (miscellaneous) 010302 applied physics Magnetic reluctance 020208 electrical & electronic engineering Cogging torque Finite element method Vibration Genetic algorithm Magnet Axial flux permanent magnet machine Reduction (mathematics) Mathematics Voltage |
| Zdroj: | Mathematics, Vol 9, Iss 1738, p 1738 (2021) Mathematics Volume 9 Issue 15 |
| ISSN: | 2227-7390 |
| Popis: | In this paper, a hexagonal magnet shape is proposed to have an arc profile capable of reducing torque ripples resulting from cogging torque in a single-sided axial flux permanent magnet (AFPM) machine. The arc-shaped permanent magnet increases the air-gap length effectively and makes the flux of the air-gap more sinusoidal, which decreases air-gap flux density and hence causes a reduction in cogging torque. Cogging torque is the basic source of vibration, along with the noise in PM machines, since it is the main cause of torque ripples. Cogging torque is independent of the load current and is proportional to the air-gap flux and the reluctance variation. Three-dimensional finite element analysis (FEA) is used in the JMAG-Designer to analyze the performance of the conventional and proposed hexagonal-shaped PM AFPM machines. The proposed shape is designed to reduce cogging torque, and the voltage remains the same as compared to the conventional hexagonal-shaped PM machine. Further, optimization is performed by utilizing an asymmetric overhang. Latin hypercube sampling (LHS) is used to create samples, the kriging method is applied to approximate the model, and a genetic algorithm is applied to obtain the optimum parameters of the machine. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|