The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Artificial Neural Network (ANN) Based Fast and Accurate Inductor Modeling and Design
Open accessAbstract
This paper analyzes the potential of Artificial Neural Networks (ANNs) for the modeling and optimization of magnetic components and, specifically, inductors. After reviewing the basic properties of ANNs, several potential modeling and design workflows are presented. A hybrid method, which combines the accuracy of 3D Finite Element Method (FEM) and the low computational cost of ANNs, is selected and implemented. All relevant effects are considered (3D magnetic and thermal field patterns, detailed core loss data, winding proximity losses, coupled loss-thermal model, etc.) and the implemented model is extremely versatile (30 input and 40 output variables). The proposed ANN-based model can compute 50'000 designs per second with less than 3% deviation with respect to 3D FEM simulations. Finally, the inductor of a 2 kW DC-DC buck converter is optimized with the ANN-based workflow. From the Pareto fronts, a design is selected, measured, and successfully compared with the results obtained with the ANNs. The implementation (source code and data) of the proposed workflow is available under an open-source license.
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Cites
Transformer and Inductor Design Handbook
McLyman, C. W. T. (2004) · CRC Press eBooks · doi:10.1201/9780203913598 · Cited by 1,369
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Neural-network-based motor rolling bearing fault diagnosis
Li, B., Chow, M.-Y., Tipsuwan, Y., & Hung, J. C. (2000) · IEEE Transactions on Industrial Electronics · doi:10.1109/41.873214 · Cited by 797
Neural Network Applications in Power Electronics and Motor Drives—An Introduction and Perspective
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Deep Reinforcement Learning for Multiobjective Optimization
Li, K., Zhang, T., & Wang, R. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2977661 · Cited by 424
Leibl, M., Ortiz, G., & Kolar, J. W. (2017) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2016.2623679 · Cited by 306
Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence
Skansi, S. (2018) · Undergraduate Topics in Computer Science · doi:10.1007/978-3-319-73004-2 · Cited by 278
100 kW, 10 kHz Medium-Frequency Transformer Design Optimization and Experimental Verification
Mogorovic, M., & Dujic, D. (2019) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2018.2835564 · Cited by 274
Model-based fault diagnosis in electric drives using machine learning
Murphey, Y. L., Masrur, M. A., Chen, Z., & Zhang, B. (2006) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2006.875568 · Cited by 165
Metadata
- Authors
- Guillod, T., Papamanolis, P., & W. Kolar, J.
- Venue
- IEEE Open Journal of Power Electronics
- Published
- 2020
- Volume
- 1
- Pages
- 284-299
- ISSN
- 2644-1314 (ISSN-L)
- OpenAlex
- W3046417006
- Cited by
- 264
- References
- 18
- FWCI
- 9.87
- Type
- journal article
- Language
- en
- Open access
- gold
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- Guillod, T., Papamanolis, P., & W. Kolar, J. (2020). Artificial Neural Network (ANN) Based Fast and Accurate Inductor Modeling and Design. IEEE Open Journal of Power Electronics. https://doi.org/10.1109/ojpel.2020.3012777
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