Bibliographic record
Abstract
Detecting faults in DC microgrids faces numerous challenges in terms of fast detection requirements, sensitivity against low- and high-resistance faults, and selectivity. This paper proposes a novel local-measurement-based DC distance relay for DC microgrids that addresses these challenges. The relay’s power circuit integrates an inductor at the end of each line. Additionally, it employs auxiliary components with a peak detection circuit (PDC) for capturing and processing different waveforms at the instant of fault occurrence. Local measurements of the relay voltages and currents are used to identify local forward faults, and to estimate the fault location within a short time frame. Furthermore, the relay provides backup protection for forward external faults on adjacent lines. The concept is first verified on a simple feeder. Then, a meshed DC microgrid, modeled in PSCAD/EMTDC environment, is used to further verify and evaluate the performance of the proposed scheme. Various fault scenarios are performed to examine the relay’s performance under different fault conditions. The results highlight the speed, selectivity, and sensitivity of the proposed method against bolted, low- and high-resistance faults.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".