Protection of multi‐terminal remote DC microgrids with overhead lines against temporary and permanent faults
Bibliographic record
Abstract
Multi‐terminal low‐voltage DC (MT‐LVDC) remote microgrids are subjected to faults with low current magnitudes. Although such faults do not disturb microgrid steady‐state operation, their continuous existence can lead to permanent (prm) power losses and personnel hazard. Yet, given that faults are often temporary (tmp) on overhead lines, instantaneous de‐energisation of the entire microgrid upon fault detection can result in prolonged loss of infeed, i.e. loss of load. This study proposes a centralised protection scheme to achieve tmp‐fault resilient MT‐LVDC microgrids. The proposed approach is comprised of local intelligent electronic devices (IEDs) at each terminal and a central IED at one of the MT‐LVDC microgrid terminals. Low‐bandwidth communication is utilised between local and central IEDs. Local IEDs are equipped with overcurrent function to take fast protective action under faults with high current magnitudes. On the other hand, the central IED consists of two passive oscillators and DC choppers. The passive oscillators facilitate the detection and identification of tmp and prm faults with low current magnitudes. DC choppers are used for online pole voltage rebalancing upon tmp fault clearance. Testing the centralised protection scheme on a , TN‐S grounded, MT‐LVDC microgrid reveals that the approach is fast, sensitive, selective and resilient under various conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".