An islanding detection method based on measuring impedance at the point of common coupling
Bibliographic record
Abstract
This paper develops an approach to islanding detection based on computing the frequency dependent impedance at the point of common coupling (PCC) that exploits the existing harmonics injected by the electric power system (EPS) and the harmonics injected by the distributed generator (DG). To this aim, a frequency dependent model is developed to characterize the change in the circuit interconnection topology, among the DG, the EPS, and the local load when the islanding occurs. The change in the topology of the system when the island occurs will result in a change in the impedance at the PCC. This approach may be used as a basis for selecting features from the impedance that distinguishes islanding from normal operation. The islanding condition can be detected when certain changes are detected in the measured impedance at different existing harmonics. A passive approach will be proposed based on monitoring how the impedance changes at the different harmonics. The approach uses the variation of the frequency dependent feature to characterize a non-detection zone (NDZ). Although the method is categorized as a passive islanding detection technique, it also can be applied as an active scheme where certain harmonics will be intentionally injected at the PCC rather than being inherent.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".