Model Predictive Switching Pattern Control for Current-Source Converters With Space-Vector-Based Selective Harmonic Elimination
Bibliographic record
Abstract
This paper presents a model predictive switching pattern control (MPSPC) for a current-source converter (CSC), which achieves superb low-order harmonics elimination performance in steady state and improved transient responses. Based on a proposed space-vector-based selective harmonic elimination (SHE) method and prediction of load current at the next sampling instant, MPSPC prefers to following a precalculated SHE-pulse width modulation (PWM) pattern in steady state, and governing the CSC through a model predictive control (MPC) approach during transients. In comparison with existing schemes, the advantages of MPSPC are threefold: First, quantization error, introduced by a constant sampling frequency in MPC and degrading steady-state low-order harmonic elimination, is mitigated in the proposed scheme. Second, there is no weighting factor in the cost function, as used in existing schemes. Finally, MPSPC is totally realized based on one-step prediction, which simplifies the structure of the scheme. Both simulation and experimental results verify the steady state and dynamic performance of MPSPC with different SHE-PWM patterns.
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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 | 0.000 |
| 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".