Islanding detection for utility interconnection of multiple distributed generators
Why this work is in the frame
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Bibliographic record
Abstract
At present, islanding detection is one of the important research orientations in the area of distributed generation (DG). There exist currently in the literature several methods for islanding detection in the case of single distributed generator connected to a utility grid. These methods are classified as active, passive and methods installed on the utility. However, these methods have limitations or become ineffective in the case of multiple distributed generators connected to the same point of the utility grid because of the interferences between sources. In this paper, we present a robust islanding detection method based on the correlation technique. Analytical and simulation studies were performed in order to validate the accuracy of the method. The results showed that this method behaves well in the case of multiple distributed generators connected to the utility grid even in the critical situations i.e. when the power of the load is provided solely by the distributed generator.
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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