A Fast and Setting-Less Breaker Failure Backup Protection Scheme for Multi-Terminal HVDC Grids
Bibliographic record
Abstract
Breaker failure backup protection schemes are essential for the reliable operation of high voltage direct current (HVDC) grids and to prevent damages to power system equipment due to sustained faults. This paper proposes a rapid, reliable, and setting-less breaker failure backup protection scheme for multi-terminal HVDC grids. The proposed scheme employs Hilbert-Huang Transform (HHT) to extract two features from local voltage measurements, namely the instantaneous frequency and energy. Based on detected outliers in the extracted instantaneous features, breaker failure events are rapidly detected. The proposed setting-less outlier-based criterion can be applied to HVDC grids with various configurations, parameters, and breaker technologies. The proposed scheme depends only on local voltage measurements to detect breaker failure events within$\text{1} \, \text{ms} $from the intended breaker trip time without requiring breaker voltage or current measurements. In addition, the proposed scheme can successfully detect breaker failure events during high-resistance faults ($1500 \, \Omega$) and in grids with small boundary reactors ($\text{10} \, \text{mH} $). Numerous simulations in PSCAD/EMTDC software environment for a four-terminal HVDC grid are executed to demonstrate the rapid and reliable performance of the proposed breaker failure backup protection scheme under severe fault conditions.
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) | 0.001 | 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".