Design and Compliance of Frequency Drifting Islanding Detection Methods with the IEEE Standard 1547.1
Bibliographic record
Abstract
Islanding is one important concern for grid connected distributed resources due to personnel and equipment safety. Preliminary design and performance assessment of inverter-resident frequency drifting islanding detection methods (IDMs) can be done by means of non-detection zones (NDZs) in a load parameter space. However, this tool only reflects the steady-state condition, that is, if the frequency of the islanded system will end up within a normal frequency range, where islanding occurs. It does not provide any information regarding the transient period, which is an important factor for the definition of the load testing points for the IEEE Std. 1547.1. Time domain simulations are used to identify critical load testing points that yield the longest run times. Then, the impact of these load values on the effectiveness of the design of frequency drifting IDMs using NDZ is discussed. As a result, an additional criterion for IEEE Std. 1547 compliance is presented.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".