Fast and reliable detection of power islands using transient signals
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
A new technique for fast detection of power islands in a distribution network, which uses transient signals generated during the islanding event is investigated. Performance comparison of several pattern recognition techniques in classifying the transient generating events as islanding or non-islanding is presented. Features for the classifiers are extracted using the Discrete Wavelet Transform of current signal transients. Using a set of extracted features from simulated current signals, (i) a decision tree classifier, (ii) a probabilistic neural network classifier, and (iii) a support vector machine classifier were trained for recognizing the transient patterns originating from the islanding events. The trained classifiers were then tested with unseen test current waveforms. The test results demonstrated that the investigated technique can potentially provide a new way for identification of islanding in distribution systems. The approach was then extended changing the feature set and sampling frequency. Proposed method is finally compared with an existing islanding detection technique.
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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