Grid Harmonics Compensation Using High-Power PWM Converters Based on Combination Approach
Bibliographic record
Abstract
For high-power pulsewidth modulation (PWM) converters, selective harmonic elimination (SHE) modulation scheme is commonly adopted to reduce the low-order harmonics caused by a low switching frequency. However, the SHE scheme itself lacks the capability to actively compensate the grid background harmonics. To enable the active compensation ability of the SHE-modulated PWM converters, a selective harmonic compensation scheme and an SHE phase jittering method have been proposed in the previous works, and their effectiveness to actively attenuate the one grid line current harmonic was verified on a high-power PWM current-source rectifier (CSR) system application. Nevertheless, both the two methods have difficulty in compensating two harmonics simultaneously, which limits their applications with a low resonant frequency of converter system's filter circuit. This paper extends the previous studies to enable the high-power PWM converters to actively compensate two grid background harmonics. The proposed method can not only further reduce the grid line current distortion but also make the application of the active compensation no longer limited by the filter circuit. The experimental results of its application on a high-power PWM CSR system are provided to verify the effectiveness.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".