Non-invasive Magnetic Sensors for Measurement of Current Phasors in Power Systems: Calibration and Validation
Bibliographic record
Abstract
Real-time current monitoring is essential for precisely monitoring the state of power grid and also for detecting faults leading to more reliability and dependability. The advances in magnetic sensors research offer accuracy in measurement systems at less complexity in installation and maintenance with less cost. Anisotropic magnetoresistive (MR) sensor can be used to measure the AC current by sensing the magnetic field generated by the current carrying conductor. This can be achieved without touching the conductor or surrounding the sensor like conventional current transformers. This paper describes a novel method of current phasor measurement with the help of a low-cost, broadband and high-sensitivity Tunneling Magnetoresistance (TMR) sensor for AC current measurement. Based on the measurements from experiment for single phase, a detail analysis for calibration and validation of the sensor is performed. The accuracy in measurement is achieved by applying various conditions such as varying distances from the conductor and frequencies from 60Hz to fifth harmonic. Satisfactory results are obtained and are presented in this paper.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".