A Pattern-Recognition Approach for Detecting Power Islands Using Transient Signals—Part II: Performance Evaluation
Bibliographic record
Abstract
Part I of this paper describes the design and implementation of an islanding detection method based on transient signals. The proposed method utilizes discrete wavelet transform to extract features from transient current and voltage signals. A decision-tree classifier uses the energy content in the wavelet coefficients to distinguish islanding events from other transient generating events. The verification tests performed in Part I, for a two generator test system having a synchronous generator and a wind farm, showed more than 98% classification accuracy with 95% confidence and a response time of less than two cycles. In Part II, the proposed methodology is applied to an extended test system with a voltage-source converter-based dc source. The proposed relay's performance is compared with the existing passive islanding detection methods under different scenarios. Furthermore, the effect of noise on the performance of the proposed method is studied. The transient-based islanding detection methodology exhibits very high reliability and fast response compared to all other passive islanding detection methods and shows that the relay can be designed with a zero nondetection zone for a particular system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".