Hybrid SHM-PWM for Common-Mode Voltage Reduction in Three-Phase Three-Level NPC Inverter
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Bibliographic record
Abstract
This article proposes hybrid selective harmonic mitigation (SHM)-pulsewidth modulation (PWM) that is characterized by both features harmonic mitigation and elimination aimed to reduce the common-mode voltage (CMV) and to mitigate the selected nontriplen harmonics in a three-phase neutral-point-clamped (NPC) inverter. As CMV harmonic modeling only shows triplens, the specified triplens are eliminated using the selective harmonic elimination (SHE) operation to control CMV pulsewidths and consequently to mitigate CMV magnitude that appears as its root-mean-square (rms) reduction. The determined nontriplen harmonics are also mitigated using the SHM approach by the same cost function. The proposed hybrid SHM-PWM empowered by both harmonic elimination and mitigation is implemented on a three-phase NPC inverter to confirm its performance on reducing CMV through experimental and theoretical analyses. It is shown that hybrid SHM is superior over pure SHE or SHM in dealing with multiobjective system, including CMV reduction and no ntriplen harmonics mitigation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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