Selective Harmonic Compensation (SHC) PWM for Grid-Interfacing High-Power Converters
Bibliographic record
Abstract
Compensating the grid background harmonics in a grid-interfacing converter system, such as a drive system's active-front-end rectifier or a grid-connected inverter in a distributed generation system, is an effective method of reducing line side current harmonics. However, this harmonic compensation is particularly challenging in medium-voltage high-power applications (>1 MVA). This is mainly due to the low-switching frequency operation of high-power converters (300-800 Hz) to maintain low power loss. Therefore, the traditional tasks of active power filters with relatively high-switching frequency cannot be easily realized here. This paper proposes a new pulse width modulation technique, named selective harmonic compensation (SHC), which actively compensates the power system background harmonics, but still operates at very low-switching frequencies. Details of the proposed SHC are presented. An SHC application example on a high-power current-source rectifier is provided in this paper. The simulations and experiments show that the proposed SHC scheme can effectively compensate the system background harmonics and improve the line current harmonic performance.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".