Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning: A Benchmark
W. Kirchgässner, O. Wallscheid, J. Böcker, IEEE Transactions on Energy Conversion 36 (2021) 2059–2067.
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Journal Article
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| English
Publishing Year
Journal Title
IEEE Transactions on Energy Conversion
Volume
36
Issue
3
Page
2059 - 2067
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Kirchgässner W, Wallscheid O, Böcker J. Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning: A Benchmark. IEEE Transactions on Energy Conversion. 2021;36(3):2059-2067. doi:10.1109/tec.2021.3052546
Kirchgässner, W., Wallscheid, O., & Böcker, J. (2021). Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning: A Benchmark. IEEE Transactions on Energy Conversion, 36(3), 2059–2067. https://doi.org/10.1109/tec.2021.3052546
@article{Kirchgässner_Wallscheid_Böcker_2021, title={Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning: A Benchmark}, volume={36}, DOI={10.1109/tec.2021.3052546}, number={3}, journal={IEEE Transactions on Energy Conversion}, author={Kirchgässner, Wilhelm and Wallscheid, Oliver and Böcker, Joachim}, year={2021}, pages={2059–2067} }
Kirchgässner, Wilhelm, Oliver Wallscheid, and Joachim Böcker. “Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning: A Benchmark.” IEEE Transactions on Energy Conversion 36, no. 3 (2021): 2059–67. https://doi.org/10.1109/tec.2021.3052546.
W. Kirchgässner, O. Wallscheid, and J. Böcker, “Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning: A Benchmark,” IEEE Transactions on Energy Conversion, vol. 36, no. 3, pp. 2059–2067, 2021, doi: 10.1109/tec.2021.3052546.
Kirchgässner, Wilhelm, et al. “Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning: A Benchmark.” IEEE Transactions on Energy Conversion, vol. 36, no. 3, 2021, pp. 2059–67, doi:10.1109/tec.2021.3052546.
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