Temperature Estimation in Induction Motors using Machine Learning

被引:1
|
作者
Li, Dinan [1 ]
Kakosimos, Panagiotis [1 ]
机构
[1] ABB AB Corp Res, Vasteras, Sweden
来源
2023 IEEE APPLIED POWER ELECTRONICS CONFERENCE AND EXPOSITION, APEC | 2023年
关键词
Induction motor; machine learning; temperature monitoring; neural network;
D O I
10.1109/APEC43580.2023.10131273
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The number of electrified powertrains is ever increasing today towards a more sustainable future; thus, it is essential that unwanted failures are prevented, and a reliable operation is secured. Monitoring the internal temperatures of motors and keeping them under their thresholds is an important first step. Conventional modeling methods require expert knowledge and complicated mathematical approaches. With all the data a modern electric drive collects nowadays during the system operation, it is feasible to apply data-driven approaches for estimating thermal behaviors. In this paper, multiple machine-learning methods are investigated on their capability to approximate the temperatures of the stator winding and bearing in induction motors. The explored algorithms vary from linear to neural networks. For this reason, experimental lab data have been captured from a powertrain under predetermined operating conditions. For each approach, a hyperparameter search is then performed to find the optimal configuration. All the models are evaluated by various metrics, and it has been found that neural networks perform satisfactorily even under transient conditions.
引用
收藏
页码:2398 / 2404
页数:7
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