DC-Link Current Ripple Mitigation for Current-Source Grid-Connected Converters Under Unbalanced Grid Conditions
Bibliographic record
Abstract
The purpose of this paper is to develop a model and propose control strategies to mitigate dc-link current ripple for current-source grid-connected converter (CSGCC) under unbalanced grid conditions. Based on the model of instant active power under unbalanced grid conditions, this paper proposes the optimized negative-sequence current references for eliminating the double-frequency oscillations on active power at ac side of CSGCC. Both the single CSGCC and the paralleled CSGCCs are considered in this paper. In order to track the asymmetric current references more accurately while maintaining LC resonance damping, the hybrid current controller is proposed by combining the closed-loop fundamental current controller under double synchronous frames and the closed-loop high-bandwidth harmonic current controller under stationary frame. The design of the hybrid current controller is analyzed. Finally, the experimental results are shown to verify that the control strategies can mitigate the dc-link current ripple effectively, and also provide good LC resonance damping performance for grid currents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".