Optimal Fault Predictors for Arc-Type Faults in Radial and Meshed Alternating Current Distribution Systems
Bibliographic record
Abstract
Efficient and reliable linear and nonlinear fault diagnostic systems are of vital importance in modern utility power grids as quick, accurate detection of faults can assist in preventing system failures that cause economic loss and endanger human or animal life. The foundation of any fault diagnostic system is its set of predictors; robust predictors naturally lead to a reliable system. This work fills the need for a deep investigation into reliable fault detection predictors. Novel harmonic-based fault predictors are developed for diagnosis of fault condition in both radial and meshed type AC distribution systems, with four fault classification groups (bolted fault, high impedance nonlinear fault, linear fault, and a no-fault classification). These new fault predictors are optimized and rigourously tested against earlier fault predictors using existing fault models and known statistical methods in both noiseless and noisy conditions.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".