Hybrid SHM-SHE Pulse-Amplitude Modulation for High-Power Four-Leg Inverter
Bibliographic record
Abstract
This paper presents a hybrid selective harmonic mitigation (SHM)-selective harmonic elimination (SHE) switching technique based on pulse-amplitude modulation (PAM) concept. It has been applied on a four-leg neutral-point-clamped (NPC) inverter to eliminate and mitigate more harmonic orders than recently proposed hybrid SHM-SHE-pulse width modulation (PWM) method while generating switching pulses at the same frequency. In conventional SHE and SHM techniques, equations are solved to attain the switching angles. Regarding the PAM, value of inverter dc voltage can be considered as an additional degree of freedom by which the flexibility of such techniques would be increased maintaining the switching frequency. In the proposed SHM-SHE-PAM, the conventional equations are reformulated to obtain constant switching angles for a vast range of modulation index (ma) applied on a four-leg NPC inverter. Switching pulses of the three-phase legs and the fourth leg are calculated to mitigate the nontriplen harmonics and eliminate the triplen ones, respectively. Due to the unique switching angles valid in the whole range for ma, the calculation time and volume (storage capacity) are significantly reduced leading to a simpler controller implementable on a low-risk and cheap AVR chip. Experimental tests' results of a four-leg NPC inverter as UPS application prove the good dynamic performance and accuracy of the proposed implemented switching technique in producing associated pulses for the inverter switches at very low frequency to mitigate/eliminate undesired harmonic orders from the output phase/line voltage waveforms without using bulky filters.
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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".