Application of μPMUs for adaptive protection of overcurrent relays in microgrids
Bibliographic record
Abstract
This study proposes a new application of micro‐phasor measurement units (µPMUs) for adaptive coordination of overcurrent relays in microgrids. Mis‐coordination of overcurrent relays usually arising from the variation of relays fault current and it can cause damage to equipment of network and raise operating costs. Fault current injection and direction to microgrid are highly dependent on network uncertainties; therefore, fault current is affected by line and power plant outages. This study proposes an algorithm to detect these uncertainties in online operation. Then, microgrid overcurrent relays coordination is optimised again. Uncertainties are line and power plant outages in transmission network and microgrid side and two distinct methods are used for each. For online detection of uncertainties in the transmission side, it is assumed that a µPMU is installed between transmission network and microgrid point of common coupling; so, the topology changes such as line outage is detected by monitoring of Thevenin impedance estimation that is obtained by µPMU measurements. Uncertainties detection in a microgrid is done by signals that are sent by µPMUs and installed all over the microgrid. All data are gathered and analysed in phasor data concentrators and then overcurrent relays coordination is updated with such changes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".