Islanding protection of multiple distributed resources under adverse islanding conditions
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
This study proposes a methodology based on multivariate analysis and data mining techniques that credibly captures the signature of the DG‐islanding phenomenon under adverse operating conditions and network faults. This methodology produces decision trees which determine the tripping logic, protection handles and thresholds for each DG‐islanding relay within the distribution network under study. Case study results indicated that the intelligent islanding relay (IIR) produced by the proposed methodology is consistently high‐level performance in terms of dependability and security, and features reduced non‐detection zones compared with the currently used islanding devices. It is also demonstrated how to determine adaptive settings to accommodate entire ranges of system operating conditions applicable to different ranges of power mismatches (overbalance, underbalanced and near balanced) at the point of common coupling bus. Experimental validation for prove‐of‐concept of the proposed IIR using hardware‐in‐the‐loop has been conducted which was consistent with the off‐line test results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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