Modulated Finite-Control-Set Model Predictive Current Control for Five-Phase Voltage-Source Inverter
Bibliographic record
Abstract
Due to the fixed and limited sampling period in the real-time system, three-phase inverters using finite-control-set model predictive current control (FCS-MPCC) usually suffer from large ripples of phase current. Moreover, the extension of FCS-MPCC scheme to a multiphase system always faces another challenge, which is to simultaneously regulate the fundamental and the low-order harmonic current components. For the case of the five-phase voltage source inverter that has been widely investigated recently, the existing literature has solved the harmonic currents issues with the utilization of virtual voltage vectors. But these virtual voltage vectors are still limited to the fixed angles and magnitudes in the space coordinate and lack effective current ripples suppression means. Therefore, this paper first applies virtual vectors to reduce the lower order harmonic currents and thus eliminates the weighting factor in cost function. Then, based on duty-cycle optimization, a modulated control set synthesized through virtual voltage vectors is employed to attain a smoother phase current waveform. Compared to existing methods, the vector selection and duty-cycle determination can be implemented simultaneously. Finally, a comparison of the proposed modulated FCS-MPCC scheme and the other FCS-MPCCs is presented. Simulation and experimental results verify that the proposed scheme can remain the simplified structure, fast dynamic performance, constant switching frequency, and achieve minimal current ripples.
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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 | 0.001 |
| 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 | 0.001 | 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".