Magnetic sensors for contactless and non‐intrusive measurement of current in AC power systems
Bibliographic record
Abstract
Abstract This paper reports the results of an investigation into the use of magnetic sensors for measuring AC currents and subsequently, estimating AC current phasors in low‐ and medium voltage AC power systems. Tunnelling magnetoresistive (TMR) sensor of high sensitivity and a wide range was used for the magnetic field measurement around AC conductor. The sensor was calibrated to overcome the inequality in the sensed magnetic field due to various aspects such as the distance from the source, minute structural variations, the magnitude of the source current, and presence of harmonics. Performance was tested for accuracy at lower frequencies such as 1, 2, 5 and 10 Hz as well as at higher frequencies such as 2nd, 3rd, 4th and 5th harmonics of the fundamental frequency. The percentage total vector error (TVE) was calculated for current phasors with input current magnitudes varying from 5 to 1500 A of various frequencies and was compared with the actual current as well as with the outputs of a high accuracy conventional core‐wound donut type current transformer (CT). The measurement accuracy corresponding to magnitude, phase and TVE during laboratory and field applications validated the suitability of TMR sensor for contactless and non‐invasive AC current measurement.
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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) | 0.000 | 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 | 0.001 | 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".