时间序列电机驱动器基于模拟建模和双向长短记忆的预测.
Thi-Thu-Huong Le1,2, Yustus Eko Oktian1,2, Uk Jo3
1Blockchain Platform Research Center, Pusan National University, Busan 609735, Republic of Korea.
本研究介绍了一种使用快速里叶变换 (FFT) 和双向长短内存 (Bi-LSTM) 网络的新方法,用于在直矩控制 (DTC) 感应电机中准确预测电信号,从而提高性能和监控.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
背景情况:
- 在三相直矩控制 (DTC) 感应电机中,准确的电信号预测对于性能优化和状态监控至关重要.
- 传统的预测方法面临的挑战是由于DTC电机的复杂性和操作的可变性.
研究的目的:
- 开发DTC感应电机中电信号的创新预测方法.
- 为了提高电机信号预测的精度和可靠性.
主要方法:
- 使用快速里埃转换 (FFT) 预处理模拟数据.
- 使用双向长短存储器 (Bi-LSTM) 网络进行信号预测.
- 对反复神经网络 (RNN),长期短期记忆 (LSTM) 和门式反复单元 (GRU) 模型进行比较分析.
主要成果:
- 提出的FT-Bi-LSTM方法在预测感应电机信号方面表现出卓越的性能.
- 实现了低的平均绝对误差 (MAE),为92.6864的稳压器电流和93.8802的转子电流.
- 显示预测损失减少,根平均平方误差 (RMSE) 平均值为105.0636和85.7820.
结论:
- FFT-Bi-LSTM方法显著提高了DTC感应电机的电信号预测的准确性和可靠性.
- 这种方法为复杂的电机控制系统提供了强大的解决方案.
- 这些发现强调了先进的人工智能技术在运动诊断和预后方面的潜力.
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