High‐Impedance Fault Detection in Electric Power Distribution Network Using Local Pattern Transformation Methods and Bidirectional Long Short–Term Memory
Bibliographic record
Abstract
The high‐impedance fault (HIF) occurring in medium voltage (MV) distribution networks is dangerous to livestock and personnel due to its arcing nature. The untimely detection of the fault can endanger lives and destroy equipment. Identifying the occurrence of HIF in a power system is a cumbersome task, as fault current falls within the normal current range. The paper analyses current signals from radial and mesh distribution networks and features extracted during HIF and non‐HIF conditions by using the local binary pattern (LBP), local neighbor gradient pattern (LNGP), local neighbor descriptive pattern (LNDP), and local gradient pattern (LGP). In the proposed algorithm, 1D signal analysis for HIF detection in the MV distribution system is performed for the first time for fault analysis. The Kruskal–Wallis test was carried out to get the best feature sets from the extracted features. HIF and non‐HIF were classified by bidirectional long short–term memory (Bi‐LSTM) for the selected feature sets. Among the four algorithms, LGP attains the best accuracy for both networks; hence, the paper recommends that LGP with Bi‐LSTM is more effective for detecting HIF occurrence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".