Multi-objective Optimization of Permanent Magnet Adjustable Speed Driver Base on RSM Model and NSGA-II
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To optimise the structure of a permanent magnet adjustable speed driver (PMASD), a multi-objective optimization design method for increasing the output torque and reducing the eddy current loss was proposed. Firstly, the three-dimensional finite element method model of a PMASD was established, the influence of the main configuration parameters in the PMASD on the output torque and the eddy current loss was analyzed, and the reasonable range of the parameters was determined. When taking the minimal eddy current loss and maximum output torque as the optimal objectives, the secondary response surface numerical model equation was built using the Central Composite Design experimental method and the Response Surface Methodology. Then, while ensuring that the output torque of the PMASD is not less than the rated torque, the NSGA-II was used to perform multi-objective optimization based on the response surface model and the Pareto optimal solution sets for two objectives was obtained. Finally, the minimal eddy current loss model and maximum output torque model were selected to compare with the initial model that was not optimized. The simulation results proved that the output torque of the minimal eddy current loss model increased by 6.54% based on the reduction of eddy current loss by 0.81% and the output torque of the maximum output torque model increased by 24.41% based on an increase in eddy current loss of 10.51%: the performance of both optimization models had been improved significantly. The optimization results show that this method improves the transmission performance of the PMASD.
KeywordsPermanent magnet adjustable speed driver (PMASD) Finite element method (FEM) Response surface methodology (RSM) model Non-Dominated Sorted Genetic Algorithm-II (NSGA-II) Multi-objective optimization
This work was supported by College Scientific Research Project of Gansu Province (2016B-113, 2016A-100) and Science and Technology Support Program of Gansu Province (1304GKCA008).
- 1.Wallace, A., Von Jouanne, A., & Jeffryes, R., et al. (2000). Comparison testing of an adjustable-speed permanent-magnet eddy current coupling. In IEEE Pulp and paper Industry Technical Conference, (pp. 73–78).Google Scholar
- 3.Ge, Y. J., Yuan, Z., Jia, F., et al. (2016). Mechanical properties and testing for squirrel cage asynchronous magnetic coupler. Transactions of the Chinese Society of Agriculture Engineering, 32(12), 68–74.Google Scholar
- 9.Wang, X., Wang, D. Z., Liu, Z., et al. (2012). Eddy current field analysis and performance calculations for adjustable permanent magnetic coupler. Chinese Journal of Scientific Instrument, 33(1), 155–160.Google Scholar
- 10.Zhu, A. N., & Meng, Z. (2017). 3D torque calculation of magnetic coupling and tis characteristic parameters analysisr. Electric Machines and Control, 21(10), 94–101.Google Scholar
- 11.Li, Z., & Wang, D. Z. (2017). Design of permanent magnet drive based on improve support vector regression. Journal of Northeast University (Natural Science), 38(2), 158–162.Google Scholar
- 13.Li, Z., Zhang, L., & Wang, Q. J. (2015). Optimal design of structure parameters of three-DOF deflection type PM motor based on response surface methodology. Transactions of China Electrotechnical Society, 30(13), 134–142.Google Scholar
- 14.Yang, Y., Ding, J. J., & Li, F. (2017). Optimization of curving performance for low floor rail vehicles based on RSM and NSGA-II genetic algorithm. Journal of the China Railway Society, 39(3), 25–39.Google Scholar
- 15.Wang, C. L., Feng, Y. M., & Ye, J. (2017). Multi-objective parameters optimization of centrifugal slurry pump based on RBF neural network and NSGA-II genetic algorithm. Transactions of the Chinese Society of Agricultural Engineering, 33(10), 109–115.Google Scholar