DC Breaker Failure Detection Scheme in Multi-Terminal HVDC Grids
Bibliographic record
Abstract
Fast detection of breaker failure events in high voltage direct current (HVDC) grids is critical to protect system equipment from severe damage due to sustained faults. This paper proposes a fast and reliable breaker failure backup protection scheme for multi-terminal HVDC grids based on a statistical-based technique. Upon fault detection, the Bayesian Model Averaging (BMA) algorithm is applied to local voltage waveforms to detect abrupt changes, from which the breaker status is inferred. Breaker failure events are quickly identified if no abrupt changes are observed. Relying only on local voltage measurements, the proposed scheme detects breaker failure events within 1 ms of the intended breaker trip time, without requiring additional breaker voltage or current measurements. Extensive simulations on a four-terminal HVDC grid are conducted to demonstrate the rapid and reliable performance of the proposed scheme under various fault conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".