A Comprehensive Review of Capacitor Voltage Balancing Strategies for Multilevel Converters Under Selective Harmonic Elimination PWM
Bibliographic record
Abstract
In recent decades, applications of selective harmonic elimination pulsewidth modulation (SHE-PWM) have been extended from two-level to multilevel converters (MLCs). For most MLC topologies, one of the main challenges of using SHE-PWM lies on capacitor voltage balancing, especially with very low switching frequency in high-power applications. Due to the broad variety of MLCs, it is of great difficulty to develop a generalized capacitor voltage balancing method under SHE-PWM that is suitable for all topologies. In order to further facilitate the industrial applications of SHE-PWM on MLCs, this article provides a comprehensive review of the state-of-the-art capacitor voltage balancing strategies for MLCs under SHE-PWM. The voltage balancing methods in this article include self-balancing control, charge amount regulation, zero-sequence harmonic adjustment, redundant switching angle sets adjustment, angle modification, selective harmonic elimination model predictive control, space voltage vectors adjustment, and redundant states adjustment. Detailed comparisons of the eight voltage balancing strategies under SHE-PWM are presented to facilitate the selections of the most suitable method for specific applications. Moreover, discussions on open questions and future trends of this topic are also presented, to motivate future research and explore new alternatives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".