A Selective Fault Clearing Scheme for a Hybrid VSC-LCC Multi-Terminal HVdc System
Bibliographic record
Abstract
A selective fault clearing scheme is proposed for a hybrid voltage source converter (VSC)-line commutated converter (LCC) multi-terminal high voltage direct current (HVdc) transmission structure in which two small capacity VSC stations tap into the main transmission line of a high capacity LCC-HVdc link. The use of dc circuit breakers (dc CBs) on the branches connecting to VSCs at the tapping points is explored to minimize the impact of tapping on the reliability of the main LCC link. This arrangement allows clearing of temporary faults on the main LCC line as usual by force retardation of the LCC rectifier. The faults on the branches connecting to VSC stations can be cleared by blocking insulated gate bipolar transistors (IGBTs) and opening ac circuit breakers (ac CB), without affecting the main line’s performance. A local voltage and current measurement based fault discrimination scheme is developed to identify the faulted sections and pole(s), and trigger appropriate fault recovery functions. This fault discrimination scheme is capable of detecting and discriminating short circuits and high resistances faults in any branch well before 2 ms. For the test grid considered, 6 kA, 2 ms dc CBs can easily facilitate the intended fault clearing functions and maintain the power transfer through healthy pole during single-pole faults.
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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) | 0.002 | 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".