An Outlier-Based Single-Ended Protection Scheme for Multi-Terminal MMC-HVDC Grids Based on Hilbert-Huang Transform
Bibliographic record
Abstract
Protection of High Voltage Direct Current (HVDC) grids is a crucial step that requires further advancements to ensure the secure integration of green renewable energy sources (RESs). This paper proposes a fast, reliable, and selective single-ended primary protection scheme for multi-terminal HVDC grids. In the proposed scheme, Hilbert-Huang Transform (HHT) is applied to local voltage measurements to extract the instantaneous frequency and energy features. Abrupt changes in these features during internal faults are detected as outliers. Simultaneous outliers in the extracted features correspond to internal faults, which offers a setting-less fault detection criterion; thus, eliminating the need for simulation-based and grid-specific thresholds. The proposed scheme can detect internal faults with high fault resistances up to$1000\;\Omega$within$\text{1}\;\text{ms}$. In addition, the proposed scheme can reliably distinguish between internal and external faults even when the boundary reactor size is as small as$\text{10}\;\text{mH}$. A four-terminal HVDC grid is simulated in PSCAD/EMTDC software, and various fault scenarios are investigated to verify the effectiveness of the proposed scheme in detecting and discriminating between internal and external faults under severe fault conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".