An Adaptable Fault Current Derivative Calculation Method for Derivative Relays in HVDC Grids
Bibliographic record
Abstract
This article presents an adaptable method for fault current derivative calculation in high voltage direct current (HVDC) grids composed of modular multilevel converters (MMCs). The proposed method can be used for current derivative calculation under different fault scenarios including pole-to-pole, pole-to-ground, and pole-to-metallic. The proposed method is adaptable as it can provide accurate fault current derivatives in various grid topologies such as symmetric monopole, asymmetric monopole with ground or metallic return, and bipole with ground or metallic return, as well as grids with different types of converters with and without fault-blocking capability including full-bridge MMCs (FB-MMCs), half-bridge MMCs (HB-MMCs), or a mix of HB- and FB-MMCs. The article also demonstrates how the proposed current derivative calculation method can be used to form a derivative relay, which is fast, selective, computationally efficient, and insensitive to fault resistance. Furthermore, using the proposed current derivative calculation method, all relay settings are analytically calculated instead of being obtained through time-consuming simulation studies. Simulation results for various fault scenarios, grid topologies, and converter configurations show that the calculation method is accurate and the presented relaying algorithm can detect various faults within 10$\mu$s, even when the fault resistance is as high as 500$\Omega$.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".