An effective feature extraction method in pattern recognition based high impedance fault detection
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
High impedance fault (HIF) is problematic in various distribution systems, specially in rural distribution feeders. The fault current of HIF is with low magnitude, non-linear, asymmetrical and random, therefore extracting useful detection features from HIF current and voltage is the key to solve this issue. This paper experiments with 246 conventional electrical features and their combinations and proposes an effective feature set (EFS) via a feature ranking algorithm utilizing simple signal processing technique of discrete Fourier transform and Kalman filter estimation. This EFS is tested in six types of distribution systems and exhibits a promising detection performance in terms of accuracy, dependability and security once a proper pattern recognition classifier is determined. Besides conventional batch learning algorithms, the proposed detection method demonstrates a significant performance in online machine learning environment. Therefore it shows the potential of processing instantaneous signals and updating its prediction model adaptively to detect more HIFs in future smart grid.
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 | 0.001 |
| 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