| 刘勇智,李杰,鄯成龙,林博闻.基于KELM-SAPSO的开关磁阻电机优化设计[J].电子测量与仪器学报,2019,33(2):148-153 |
| 基于KELM-SAPSO的开关磁阻电机优化设计 |
| Optimization design of switched reluctance motor based on kernel extreme learning machine and simulated annealing particle swarm optimization |
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| 中文关键词: 开关磁阻电机 核极限学习机 模拟退火粒子群算法 平均转矩 转矩脉动 |
| 英文关键词:switched reluctance motor kernel extreme learning machine simulated annealing particle swarm optimization average torque torque ripple |
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| 中文摘要: |
| 开关磁阻电机由于其双凸极结构和铁芯磁通密度的高度饱和性，使得建立准确的非线性模型极其困难，传统的设计方法难以设计出最优方案。为了优化在起动过程中开关磁阻起动/发电机的转矩特性，缩短起动时间，首先采用传统设计方法设计了一台开关磁阻电机，并且选取了设计参数；然后针对非参数建模结构简单，容易辨识的特点利用核极限学习机进行非参数建模；最后使用模拟退火粒子群算法对平均转矩和转矩脉动进行多目标寻优。仿真结果表明，建立的非参数模型拟合精度高，优化后电机的平均转矩增加了395 N·m，转矩脉动减少023。仿真实验表明，核极限学习机和模拟退火粒子群相结合的算法适合于电机的设计与优化过程，具有参数设置少、泛化能力强、不易陷入局部最优解、耗时较少等优点。 |
| 英文摘要: |
| It is difficult to establish an accurate nonlinear model for switched reluctance motor with double salient structure and high saturated magnetic field, and traditional design methods are difficult to obtain optimal scheme. In order to optimize the torque characteristics of switched reluctance motor (SRM) and shorten the starting time of the engine, firstly, a SRM is designed with traditional methods and the design parameters are selected. Then, the kernel extreme learning machine (KELM) is used to establish a non parametric model of the SRM. Finally, simulated annealing particle swarm optimization (SAPSO) is used to optimize the structural parameters of the motor for higher average torque and lower torque ripple. Simulation results show that the model has the advantage of a higher precision and a faster speed of regression. The average torque is increased by 395 N·m, and the torque ripple is decreased by 023. The conclusion is that the combination of the KELM and the SAPSO is suitable for the design and optimization of the motor, and it has fewer regulation parameters, strong generalization, avoidance of local optimal solution and less time consuming. |
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