A Multistage Passive Islanding Detection Method for Synchronous-Based Distributed Generation
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Bibliographic record
Abstract
A multistage approach to passive islanding detection is proposed that utilizes a decision tree (DT) like classification algorithm. The novelty of the proposed method is centered on the way in which features are passed to subsequent stages of the DT. Feature sets extracted using different sized time windows are passed to successive stages of the tree. This provides two important advantages: 1) cases that can be easily determined as either islanding or nonislanding events are flagged as soon as possible without waiting for the full feature set to become available; 2) because the algorithm allows for the use of different sized time windows, features are analyzed in time-scales that fit their natural patterns of temporal evolution. In this article, the proposed classifier is trained and tested using a database of feature vectors, obtained using PSCAD, which were designed to reflect a variety of commonly encountered events on an IEEE 34-bus distribution system. One of the key requirements for the proposed algorithm was that easy cases should be flagged as soon as possible; this property was confirmed by the observation that most events ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 79%) were detected within 10–20 ms, while at the same time retaining a very high detection rate overall cases ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$>\!99$</tex-math></inline-formula> %).
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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