Extracting the Phase of Fault Currents: A New Approach for Identifying Arc Flash Faults
Bibliographic record
Abstract
This paper proposes a new approach for detecting and identifying arc flash faults in power systems. The proposed approach is structured to extract the phases of transient frequency components present in arc flash fault currents. The desired phases can be extracted by processing fault currents using a modulated filter bank that is composed of high-pass finite impulse response (FIR) filters. These filters are designed by using the Kaiser window method to achieve linear phase responses. Extracting the phases of transient frequency components, present in arc flash fault currents, can provide signature information for accurate and fast detection and distinguish of an arc flash fault. The proposed phase-based approach is implemented for off-line testing to evaluate its performance. Test cases of parallel and series arch flash faults are conducted for supplying linear, nonlinear, and dynamic loads. Simulation and off-line results demonstrate the validity, accuracy, speed, and reliability of the phase-based approach to detect and distinguish arc flash faults with minor sensitivities to the load type and arch flash type.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".