Machine-Learning-Based Condition Monitoring of Power Electronics Modules in Modern Electric Drives
Author:
Affiliation:
1. ABB, Zurich, Sweden
2. ABB Corporate Research Center, Zurich, Sweden
3. KTH Royal Institute of Technology, Stockholm, Sweden
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/6570649/10059331/10059358.pdf?arnumber=10059358
Reference13 articles.
1. A unified approach to interpreting model predictions;lundberg;arXiv 1705 07874,2017
2. Deep Residual Convolutional and Recurrent Neural Networks for Temperature Estimation in Permanent Magnet Synchronous Motors
3. Rotor Temperature Modeling of an Induction Motor using Subspace Identification**This work was supported by the German "Federal Ministry of Education and Research" (BMBF) within the priority program "ICT2020" under project funding reference number "16N11869".
4. Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges
5. Towards Intelligent Power Electronics-Dominated Grid via Machine Learning Techniques
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1. Real-Time Digital Mapped Method for Sensorless Multitimescale Operation Condition Monitoring of Power Electronics Systems;IEEE Transactions on Industrial Electronics;2024-04
2. Reliable Thermal Monitoring of Electric Machines through Machine Learning;2023 IEEE International Conference on Prognostics and Health Management (ICPHM);2023-06-05
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