https://github.com/otvam/magnet_webinar_eqn_models
2023 MagNet Challenge Webinar: Equation-based Baseline Models
https://github.com/otvam/magnet_webinar_eqn_models
core ferrite ieee igcc igse loss magnet pels power-electronics
Last synced: 3 months ago
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2023 MagNet Challenge Webinar: Equation-based Baseline Models
- Host: GitHub
- URL: https://github.com/otvam/magnet_webinar_eqn_models
- Owner: otvam
- License: mit
- Created: 2023-05-01T19:13:14.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-06T23:04:33.000Z (over 1 year ago)
- Last Synced: 2023-11-08T06:10:46.274Z (over 1 year ago)
- Topics: core, ferrite, ieee, igcc, igse, loss, magnet, pels, power-electronics
- Language: MATLAB
- Homepage:
- Size: 7.42 MB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# MagNet: Equation-based Baseline Models
## Introduction
This repository contains the **slides** and **code** related to the following **webinar**:
* **2023 MagNet Challenge Webinar: Equation-based Baseline Models**
* **IEEE PELS Webinar - May 12 2023**
* **Thomas Guillod** - Dartmouth College
This webinar focuses on equation-based loss models for soft-magnetic materials:
* Several models are presented (SE, iGSE, ISE, iGCC, and Stenglein equation).
* The model performances are evaluated for different frequencies, waveshapes, and temperatures.
* The advantages and drawbacks of equation-based models and machine learning models are discussed.
* A MATLAB implementation of the iGSE and iGCC is discussed in detail and the pitfalls are highlighted.
## Main Files
* [slides.pdf](slides.pdf) - Slides of the webinar
* [paper.pdf](paper.pdf) - APEC paper introducing the iGCC
* [run_igse.m](run_igse.m) - Parametrize and evaluate the iGSE model
* [run_igcc.m](run_igcc.m) - Parametrize and evaluate the iGCC model
## Dataset
* For the software implementation, the EPCOS/TDK N87 ferrite material is considered.
* The material is measured at ambient temperature (25C) without DC bias.
* For parametrizing the models, the following dataset is used:
* 346 symmetric triangular waveforms (50% duty cycle)
* Dataset contained in [N87_25C_fit.mat](data/N87_25C_fit.mat)
* For evaluating the models, the following dataset is used:
* 2446 asymmetric triangular waveforms (10% to 90% duty cycle)
* Dataset contained in [N87_25C_eval.mat](data/N87_25C_eval.mat)
* Both datasets are extracted from the following repository:
* Guillod, T. and Lee, J. S. and Li, H. and Wang, S. and Chen, M. and Sullivan, C. R.
* Calculation of Ferrite Core Losses with Arbitrary Waveforms using the Composite Waveform Hypothesis: Reproducibility Dataset
* Zenodo Repository, 2022
* [10.5281/zenodo.7368936](https://doi.org/10.5281/zenodo.7368936)
## Warnings
> **Warning**
> This implementation is provided for **pedagogical purposes**:
> * The goal of this code is to highlight the **typical workflow** of equation-based loss models.
> * The implementation is **not meant** to be **comprehensive and/or accurate**.
> **Warning**
> In order to limit the complexity of the code, **several assumptions** are made:
> * Single material measured at ambient temperature
> * Only triangular signals are considered
> * No DC bias and relaxation effects
> * Simple model parametrization
> * Reduced dataset size
## Compatibility
* Tested with MATLAB R2021a and R2023a.
* The `optimization_toolbox` is required.
* The `signal_toolbox` is required.
* The `statistics_toolbox` is required.
## Author
**Thomas Guillod** - [GitHub Profile](https://github.com/otvam)
## License
This project is licensed under the **MIT License**, see [LICENSE.md](LICENSE.md).