Improved active frequency drift anti-islanding method with lower total harmonic distortion
Bibliographic record
Abstract
As more distributed generators join the utility grid, the concern of unintentional islanding increases. This concern is due to the safety hazards this phenomena imposes on the personnel and equipment. Passive anti-islanding methods monitor grid parameters to detect islanding, whereas active methods inject perturbation into current waveform to drive theses parameters out of limit. The performance of active methods, such as conventional active frequency drift method (AFD), is limited by the amount of total harmonic distortion (THD) they inject into the grid. In this paper a novel anti-islanding method is presented, which generates 30% less THD than the AFD, which results in faster island detection and better non-detection zone. The performance of the proposed method is derived analytically, simulated using MATLAB and verified experimentally using a prototype setup. A single phase grid-tied photovoltaic distributed generation system is used for the simulation and experimental setup, and considered as potential application.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".