| Autor: |
Ryan Hughes, Thomas Haidinger, Xiaoze Pei, Christopher Vagg |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Energy and AI, Vol 14, Iss , Pp 100288- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2666-5468 |
| DOI: |
10.1016/j.egyai.2023.100288 |
| Popis: |
Accurate estimation of the internal temperatures of electric machines is critical to increasing their power density and reliability since key temperatures, such as magnet temperature, are often difficult to measure. This work presents a new machine learning based modelling approach, incorporating novel physically informed feature engineering, which achieves best-in-class accuracy and reduced training time. The different features introduced are proportional to sources of machine losses and require no prior knowledge of the machine, hence the models are completely data driven. Evaluation using a standard experimental dataset shows that modelling errors can be reduced by up to 82.5%, resulting in the lowest mean squared error recorded in the literature of 2.40 K2. Additionally, models can be trained with less training data and have lower sensitivity to data quality. Specifically, it was possible to train a loss enhanced multilayer perceptron model to a mean squared error |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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