Bibliographic record
Abstract
High impedance faults are generally not detected by conventional protection functions like over current, ground fault, distance, differential etc. because of the magnitude of impedance involved in the fault path and the nature and characteristic of the fault current are special and different than the conventional fault current profiles. Each type of high impedance fault is unique in terms of magnitude of fault current, nature, characteristic and waveshape. Majority of the high impedance faults are single phase to ground faults but this can involve phase to phase elements as well. Because of the inability of the conventional protection functions to detect high impedance faults especially high impedance phase to ground faults, the electrical conductor remains live under such condition and as can be imagined, poses a huge and significant risk to wild life and more importantly human life. Atmospheric and geographical conditions have a significant role to play in high impedance phase to ground faults since they have a direct impact on the magnitude and characteristic of the fault current. This paper describes different techniques to detect high impedance phase to ground faults and focuses on the proven algorithms that have been implemented in protection relays, had been verified by real site tests and fault inception on live power lines. The paper is broadly organized as a tutorial. The characteristics of high impedance faults and the challenges involved in detecting them are described first. The paper then goes on to detail some of the important techniques in use for high impedance fault detection highlighting their strengths and weaknesses, and some modern approaches proposed to improve the dependability of protection schemes. In particular, a technique combining the fundamental and harmonic analysis of the fault waveform is presented, along with its performance in field trials carried out in co-operation with utilities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.041 |
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".