Analysis and Reduction of Zero-Sequence <i>LC</i>-Resonant Current in DPWM-Modulated 3Φ <i>LCL</i>-Type Grid-Connected Inverter With Neutral Line
Bibliographic record
Abstract
Discontinuous pulsewidth modulation (DPWM) is often adopted in the three-phase (3Φ) grid-connected inverter (GCI) for enhancing the efficiency. However, DPWM will increase common voltage at the neutral point of the filter capacitors and may deteriorate the safe operation of the inverter. To reduce the common voltage, some applications connect the neutral point of the filter capacitors and midpoint of dc voltage. Nevertheless, there may be zero-sequenceLC-resonant current in the filter inductor, leading to a larger current ripple and further increasing the power loss. This letter demonstrates the inherent mechanism how DPWM generates the zero-sequence resonance current. On the basis, this letter unveils the straightforward ideas for problem solving, regardless of the DPWM method, and then proposes an easy-implementing solution to reduce it from the perspective of control method, which is different from the state-of-the-art works by modifying modulation method, yet can reserve the efficiency improvement brought from DPWM. Finally, experimental results based on a 3.6-KVA 3Φ GCI prototype are provided to verify the analysis and effectiveness of the proposed method.
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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) | 0.000 | 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 | 0.001 | 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".