An Anti-Islanding Screening Methodology for Distributed Energy Resources in Utility Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As the penetration of distributed energy resources (DERs) continues to increase in power systems, utilities are faced with the challenge of mitigating the risks associated with unintentional islanding. This paper proposes a robust screening methodology with statistical analysis to assist utilities in determining the necessity of advanced protection measures, such as direct transfer trip (DTT), while minimizing the implementation of costly and unnecessary mitigations. The proposed methodology is validated through an extensive review of industry standards and guidelines, along with a detailed data analysis of minimum-load feeders and DER installations in a Canadian distribution utility. The results demonstrate its effectiveness in enhancing system security while minimizing unnecessary mitigation costs.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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