Antiislanding Protection Based on Signatures Extracted From the Instantaneous Apparent Power
Bibliographic record
Abstract
This paper proposes a new passive antiislanding method for three-phase (3φ) distributed generation units (DGUs). The proposed method is based on extracting signatures from the instantaneous 3φ apparent powers determined at the point of common coupling (PCC). This new method is found on the fact that the instantaneous 3φ apparent powers have components continuously exchanged between loads and sources. The islanding condition creates transient high-frequency components in the instantaneous 3φ apparent powers.These high-frequency components contain signature information capable of identifying the islanding condition. These transient high-frequency components can be extracted using the wavelet packet transform (WPT), when applied to the direct and quadrature (d-q-axis) components of the instantaneous 3φ apparent powers. The d-q WPT-based antiislanding method is implemented for testing on a 3φ permanent magnet generator-based wind energy conversion system. Test results demonstrate an accurate, fast, and reliable response to the islanding condition occurring when supplying different load types at different levels of power delivery to the host grid.
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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 | 0.001 | 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) | 0.001 | 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".