Multiobjective PWM Strategy With Independent ZSC Suppression, CMV Variation Elimination, and NPV Balance for OEWIM Fed by Dual Three-Level VSI
Bibliographic record
Abstract
In an open-end winding (OEW) drive fed by dual three-level voltage source inverters (dual-3L VSI) with a common dc bus, the zero-sequence current (ZSC), the common-mode voltage (CMV) as well as the neutral-point voltage (NPV) are three critical factors needing to be considered simultaneously. ZSC can cause torque ripples and additional losses. CMV can produce shaft current, contributing to bearing failure. NPV adversely affects the quality of the output waveform and voltage safety of the power electronic tubes. However, how to achieve these three objectives simultaneously is still an open question. To solve it, a multi-objective pulse width modulation (MOPWM) strategy is proposed. In this strategy, the vectors with zero CMV amplitude are selected at first to eliminate CMV variation. Following that, some redundant vector pairs consisting of two of these vectors are built according to their spatial positions and their effects on the ZSC and the NPV, which provides the degree to control ZSC or NPV independently. As a result, the conflicts among these objectives are resolved. Moreover, a compensation scheme is proposed to address the negative effect of the dead time on the CMV variation. Finally, the feasibility and the effectiveness of the MOPWM strategy are validated through experiments.
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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) | 0.001 | 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) | 0.001 | 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".