Improved Model Predictive Control With Reduced DC-Link Capacitor RMS Current for Back-to-Back Converter-Fed PMSM Drives
Bibliographic record
Abstract
A low root mean square (RMS) current flowing through the intermediate dc-link capacitor is always desired in the back-to-back (BTB) voltage source converters (VSCs)-fed motor drive, such that the designed capacitor lifespan can be extended or a lower dc-link size could be the alternative with improved power density. The conventional modulator-based control involves the inherent carrier-based switching operation, and hence the dominant harmonics at carrier frequency multiplier and carrier sideband are introduced into the capacitor current. Therefore, the finite-control-set model predictive control (FCS-MPC) is proposed to optimize the capacitor RMS current. The switching pulse is generated directly without using the carrier, which can give a distinctive capacitor current spectrum and more possible current pulse cancelation between the BTB VSCs can be conveniently achieved through the cost function. Besides, the acceptable grid-current quality and the motor performance are maintained by using a tunable weighting factor. The simulated and experimental results highlight the effectiveness and benefits of the proposed method in terms of 20%–35% capacitor RMS current reduction compared to the synchronous carrier-based space vector pulsewidth modulation scheme and also significant improvements over the distributed predictive manner.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".