A Generalized Selective Harmonic Elimination PWM Formulation With Common-Mode Voltage Reduction Ability for Multilevel Converters
Bibliographic record
Abstract
In this article, a generalized selective harmonic elimination pulsewidth modulation (SHE-PWM) formulation with common-mode voltage (CMV) reduction ability for multilevel converters (MLCs) is presented. The CMV is suppressed by regulating the low-order dominant zero-sequence harmonics (ZSHs) of the three-phase SHE-PWM waveforms. Two formulations are included in the proposed model to achieve the full range operation objective, i.e., with zero low-order ZSHs in low and medium modulation index (ma) range and with an optimal third-harmonic injection in high marange. With the proposed formulation, the amplitude of CMV can be effectively reduced for all types of MLCs over the whole marange. Besides, two kinds of solving algorithms, i.e., off-line and real-time based, are introduced to provide efficient solution tools targeted at the proposed model. In this article, a case study with three-level neutral-point clamped inverters is discussed in detail to better illustrate the proposed formulation and the coupling effects between the CMV reduction and capacitor voltage balancing objectives of MLCs. Simulation and experimental results based on multiple MLC topologies are carried out to validate the effectiveness of this generalized SHE-PWM formulation with reduced CMV values.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".