Application of μPMUs for adaptive protection of overcurrent relays in microgrids
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it