Stability Analysis of a Remote DC Subgrid/Microgrid Connected to a Very Weak AC Grid
Bibliographic record
Abstract
Instability issues can arise due to the high penetration of remote voltage source converter (VSC)-interfaced DC microgrid (MG) to the ac weak grid (WG), in which the grid impedance is large. This is due to the dynamic interaction between the VSCs and the WG impedance. In this work, a small-signal analysis is conducted to derive the full-order linearized model of the VSC-WG interconnection. Furthermore, a participation factor analysis is presented to identify the effect of varying the grid impedance on the VSC-WG dominant modes in the inversion and rectification modes of operation. It is found that although the system is initially stable in both modes, it tends to move toward the unstable region when the grid impedance increases. In this study, the initial locations of the corresponding dominant eigenvalues are fairly similar for both modes. However, unlike previous works, it is shown that the dominant modes in the rectification mode are much more sensitive to the grid impedance variation than the inversion mode. Time-domain simulations are conducted on a 7.25 MW dc MG which is interfaced to the ac grid via a VSC system to verify the validity of small-signal analysis in both modes.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".