Multirate Harmonic Compensation Control for Low Switching Frequency Converters: Scheme, Modeling, and Analysis
Bibliographic record
Abstract
Using the grid-interfacing voltage-source converters (VSCs) to perform harmonic control as a smart ancillary function is increasingly studied in recent years. Different from the traditional dedicated harmonic compensation converters, the main function of the high-power smart converters is still delivering real power at a low switching frequency (e.g., 2 kHz). It is challenging to compensate harmonics with low switching frequency VSCs due to the low sampling rate and large system delay. This article proposes a multirate control scheme and derives the accurate multirate model to improve harmonic control. The multirate control scheme consists of high-rate harmonic control and synchronous sampled fundamental control. With the proposed control scheme, the advantages of the conventional synchronous sampling method and the benefits of the high sampling rate are achieved simultaneously. To accurately model the low switching VSC with two different sampling rates, the lifting method is introduced to achieve the discrete-time system model for this linear periodic time-varying system. The frequency response and stability analysis are carried out based on the lifted model. The modeling method, as well as the enhanced harmonic control performance of the multirate control structure, is validated in the experiments.
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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 | 0.001 | 0.001 |
| 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".