Analysis and Dynamic Performance Improvement of Grid-Connected Voltage–Source Converters Under Unbalanced Network Conditions
Bibliographic record
Abstract
The energy sector is moving toward an extensive utilization of distributed and renewable energy resources. Such resources are usually interfaced to power grids via voltage-source converters (VSCs). Due to the increased penetration level of VSC-interfaced resources, the utilization of interfacing VSCs to support host grids under unbalanced conditions (e.g., due to grid voltage unbalance, unbalanced load conditions and unsymmetrical faults) becomes essential. However, detailed dynamic analysis and systematic design procedure to enhance the dynamic performance of grid-connected VSCs equipped with grid-support controllers are not reported in the literature. To fill in this gap, this paper presents a detailed small-signal model and analysis of the dynamics of a grid-connected VSC equipped with the recently developed balanced positive-sequence control and positive/negative-sequence control methods to support the grid under unbalanced conditions. The effects of the short-circuit ratio, angle of the ac system impedance, and phase-locked-loop parameters on the transient behavior of the VSC are thoroughly studied and characterized. Furthermore, to improve the dynamic performance of grid-connected VSCs, a simple yet effective current-control-based compensator is developed to mitigate possible instabilities associated with the low-voltage operation. Comparative simulation and experimental results validate the theoretical analysis and the effectiveness of the proposed compensation scheme.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".