通过当前重塑神经网络来降低开关抗拒电机的扭矩波动
Benqin Jing1, Guofu Liang2, Xuanju Dang3
1College of Artificial Intelligence, Guilin University of Aerospace Technology, Guilin, 541004, China.
Scientific reports
|May 20, 2025
概括
本研究引入了电流重塑神经网络 (CRNN),以显著降低交换阻抗电机 (SRM) 中的扭矩波动. CRNN优化了电流配置,以实现更顺的电机运行.
科学领域:
- 电气工程 电气工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 开关抗拒电机 (SRM) 广泛使用,但受到高扭矩波动的影响.
- 扭矩波纹限制了SRM的性能和应用范围.
研究的目的:
- 提出和验证一种新的电流重塑神经网络 (CRNN),用于缓解SRM中的扭矩波动.
- 通过精确的电流控制,提高SRM的运行流性和效率.
主要方法:
- 使用间接扭矩控制方法分析电流和电磁扭矩之间的关系.
- 开发CRNN以模拟电流并产生精确的相位电流.
- 实现基于总电流和转子角度的隐式函数的CRNN,通过比例差异化补偿电流进行调整.
主要成果:
- 通过修改相位电流配置,CRNN有效地降低了扭矩波动.
- 在三相12/8 SRM上的模拟和实验证明了该方法的有效性.
- 在各种操作条件下观察到一致的性能.
结论:
- 拟议的CRNN是减少SRM的扭矩波动的可行解决方案.
- 这种方法可以提高SRM的性能,从而实现更广泛的应用.
- 该CRNN提供精确控制电流波形,以提高电机的运行.
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