An On-Line Selective Harmonic Elimination Modulation Scheme for High-Power Medium-Voltage Current Source Converters
Bibliographic record
Abstract
In high-power, medium-voltage (MV) pulse width modulated (PWM) current source converter (CSC) based drives, selective harmonic elimination (SHE) configured with amplitude modulation index control is commonly used to modulate the front-end PWM current source rectifier (CSR), as it possesses the ability to eliminate problematic low-order harmonics, while also offering full control over the DC-link current. To practically implement this variant of SHE, the switching angles solved for at discrete values of the amplitude modulation index, across its full operating range, must be pre-computed off-line, and then stored within a look-up table on the digital controller. As a result of this off-line implementation, conventional SHE configured with amplitude modulation index control features poor dynamic performance and requires the use of a large, memory-exhaustive look-up table. This paper presents an on-line SHE scheme that models each of the necessary independent switching angles as polynomial functions of the amplitude modulation index using curve-fitting techniques. This method of implementation allows for the switching angles to be accurately recomputed in real-time, thus offering improved dynamic performance and eliminating the need for a large look-up table. Additionally, the inherent benefits of conventional SHE are retained. Experimental verification proving the effectiveness of the proposed on-line SHE scheme is provided.
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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) | 0.003 | 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".