[1]岑 岗,蔡永平,岑跃峰.基于深度学习的永磁同步电机温度预测模型[J].浙江科技学院学报,2022,(03):216-224.[doi:10.3969/j.issn.1671-8798.2022.03.003 ]
 CEN Gang,CAI Yongping,CEN Yuefeng.Temperature prediction model for permanent magnet synchronous motors based on deep learning[J].,2022,(03):216-224.[doi:10.3969/j.issn.1671-8798.2022.03.003 ]
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基于深度学习的永磁同步电机温度预测模型(/HTML)
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《浙江科技学院学报》[ISSN:1001-3733/CN:61-1062/R]

卷:
期数:
2022年03期
页码:
216-224
栏目:
出版日期:
2022-06-16

文章信息/Info

Title:
Temperature prediction model for permanent magnet synchronous motors based on deep learning
文章编号:
1671-8798(2022)03-0216-09
作者:
岑 岗蔡永平岑跃峰
(浙江科技学院 信息与电子工程学院,杭州 310023)
Author(s):
CEN Gang CAI Yongping CEN Yuefeng
(School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, Zhejiang, China)
关键词:
永磁同步电机 温度预测 嵌套式门控循环单元 卷积神经网络
分类号:
TP183; U469.72
DOI:
10.3969/j.issn.1671-8798.2022.03.003
文献标志码:
A
摘要:
为有效预测永磁同步电机的温度,首先使用嵌套结构对门控循环单元(gated recurrent unit,GRU)进行改进,提出一种嵌套式门控循环单元(nested gated recurrent unit,NGRU)网络,NGRU能对相关温度特征中的噪声进行过滤,并挖掘温度随时间变化的规律,再经过非线性变换提取深层的温度特征; 然后提出一种新型深度学习模型,即一维卷积神经网络(1D convolutional neural networks,1D-CNN)串联NGRU(1D-CNN tandem NGRU,CNGRU),CNGRU利用1D-CNN对输入特征进行初步提取,得到多角度的永磁同步电机相关温度特征作为NGRU的输入,以串联的结构融合二者的优势,得到永磁同步电机的预测温度。试验结果表明,对比其他循环网络在定子轭、定子齿和定子绕组温度上的预测表现,NGRU均方误差平均降低12.44%,无穷范数平均降低0.361 9; CNGRU在此基础上比NGRU均方误差平均降低13.29%,无穷范数平均降低0.579 6。CNGRU比NGRU及其他循环网络对永磁同步电机温度的预测,具有更高的精度和稳定性,这为保证电机的安全稳定运行提供了技术保障。

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2021-06-10
基金项目:教育部人文社会科学研究一般规划基金项目(17YJA880004)
通信作者:岑 岗(1959— ),男,浙江省象山人,教授,主要从事教育信息科学与技术和人工智能研究。E-mail:gcen@163.com。
更新日期/Last Update: 2022-06-30