Model predictive control scheme with active damping function for current source rectifiers
Bibliographic record
Abstract
This study presents a model predictive control (MPC) with active damping function for current‐source rectifiers (CSRs). Since optimal modulating vector selection process of MPC leads to spread harmonic distribution in pulse‐width‐modulated (PWM) waveform, inductor‐capacitor (LC) resonance can be easily excited. Conventional MPC was designed without the consideration of active damping, since it is for low‐power CSRs with large line resistors physically connected inside the circuit, whereas the proposed scheme achieves active damping function through a specially developed cost function, which realizes active damping function even for high‐power CSRs with lightly damped LC circuit. In comparison with conventional MPC, the advantages of the proposed scheme are three‐fold: first, the proposed scheme is totally based on MPC concept, without conventional linear controller used. Second, with the active damping term added in the developed cost function, active damping effect can be involved into MPC, which extends MPC to high‐power CSRs with lightly damped LC circuit. Last but not least, the proposed scheme is realized in the dq‐axis synchronous frame, which allows the use of simple low‐pass filters instead of complex band‐stop filters to obtain the damping current.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".