Passive Filter Design to Mitigate Dead-Time Effects in Three-Level T-Type NPC Transformerless PV Inverters Modulated with Zero CMV PWM
Bibliographic record
Abstract
Common-mode voltage (CMV) can cause common-mode (CM) current in transformerless PV inverters. In practice, large CM choke is usually required to suppress the CM current. Alternatively, zero common-mode voltage (ZCMV) modulation can be applied to eliminate the CMV. But in practical applications, CMV spikes are inevitable due to the dead-time effect. Such dead-time effect can be mitigated by special PWM sequence design, however, at the expense of increasing switching losses. In this paper, our study addresses this issue by using the DC neutral-point connected LCL (NC-LCL) filter. As the differential-mode (DM) output quality is undermined with ZCMV PWM, the LCL filter parameters must be carefully designed, where both CM and DM filters need to be considered. In addition, filter design for conventional phase disposition (PD) modulation is also included as a comparison. Both simulation results and experimental results are provided for verification.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".