Black-Start and Hot-Swap Performance Assessment and Improved Control Strategy for Grid-Forming VSC and CSC-Based PV Systems
Bibliographic record
Abstract
The grid-forming (GFM) control of voltage-source converter (VSC)-based photovoltaic (PV) systems has shown promise in supporting the grid frequency and inertia. However, the literature lacks GFM control development for current-source converter (CSC)-based PV systems and comparisons with the VSC counterpart. In particular, previous studies did not address dynamic performance assessment and comparison under critical operating conditions, such as black-start (autonomous power system restoration) and hot-swap capability (the transition between isolated and grid-tied modes). Furthermore, previous research did not address the dc- and ac- side stability differences among VSC- and CSC-based GFM PV systems. This paper addresses these research gaps by differentiating the dynamic performance and stability of GFM VSC- and CSC-based PV systems under different operating conditions. Further, this paper presents active compensators for both GFM systems to enhance their dynamic performance and stability. Compared to the GFM VSC, the GFM CSC provides a better frequency profile under black-start and hot-swap conditions, improved robustness under grid impedance variation, and inherent fast current limitation under faults. Detailed offline and real-time simulation results validate the comparative analysis and the effectiveness of the proposed active damping methods for both GFM systems.
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) | 0.000 | 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 | 0.000 | 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".