HVDC grid protection algorithm design in phase and modal domains
Bibliographic record
Abstract
To meet the required operation speed for protection of meshed voltage source converter (VSC) high‐voltage direct current (HVDC) grids, travelling wave‐based algorithms operating in the submillisecond time‐frame can be used. The domain in which these algorithms operate, i.e. modal or phase, determines their performance in fault discrimination, fault type classification, and faulted pole selection. In the recent literature, high‐speed algorithms have been proposed for various VSC HVDC grid configurations and transmission line types; yet, the choice of domain has received insufficient attention. This study offers recommendations for the choice of domain for protection algorithm design of HVDC overhead line or cable systems in symmetric monopolar and bipolar configurations. The theoretical analysis of this study, which is based on fundamental wave propagation theory, indicates that the preferred domain for protection algorithms for cable and overhead line systems are the phase and modal, respectively. Furthermore, the study provides comprehensive guidelines to construct detection functions for both configurations and discusses the errors introduced by approximations. Finally, study results from a bipolar overhead line test system demonstrate the advantages of modal over phase domain for fast fault discrimination and classification and illustrate practical problems associated with non‐ideal detection functions.
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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) | 0.000 | 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) | 0.002 | 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".