Multirate Modeling and Predictive Control for WBG-Device-Based High-Switching-Frequency Power Converters
Bibliographic record
Abstract
With the growth of wide-bandgap devices, it is necessary to exploit the high-switching-frequency benefits to improve the performance of power converter, thus requiring a higher sampling/interrupt frequency in digital signal processors. However, such a short interrupt time duration imposes big computational difficulty in the execution of programming code, especially using the model-predictive control (MPC). Thus, most of the existing MPCs are applied with switching frequencies below 20 kHz, which cannot exploit the full potential of the wide-bandgap-device-based power converters. To solve this challenge, this article proposes a multirate MPC scheme, where the trigger of interrupt and switching device transition can be performed at different rates. Compared with conventional MPCs, the main uniqueness of the proposed multirate MPC is that the high-dimensional control sequence is solved and applied within each interrupt interval. Therefore, the increased switching frequency objective can be easily achieved with a low sampling/interrupt frequency configuration, which also significantly relieves the digital execution of the heavy interrupt tasks. The proposed method shows a more optimized control input and higher computational efficiency over the multirate finite-control-set MPC counterpart. A silicon-carbide-inverter-fed ac motor drive system is used to verify the proposed multirate MPC. The results show the improved system performance with the combined advantages of both the high switching frequency and the MPC strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".