Magnetic Field Monitoring on HVdc Transmission Lines Using a UHF-RFID Tag
Bibliographic record
Abstract
High-voltage direct current (HVdc) transmission lines are gaining more attention as an integral part of modern power system networks. Monitoring the dc current is important for metering and development of dynamic line rating control schemes. However, this has been a challenging task and there is a need for remote sensing methods with high accuracy and dynamic range. Conventional methods require direct contact with the high-voltage conductors and utilize bulky and complex equipment. In this paper, we introduce a UHF radio frequency identification (RFID)-based sensor to monitor the dc current of an HVdc transmission line. The sensor is comprised of a passive RFID tag, with a custom design antenna, integrated with a Hall effect magnetic field device. The dc current is measured by monitoring the dc magnetic field around the conductor using the Hall effect device. The internal memory of the RFID tag is encoded with the magnetic field data. The RFID tag enables remote wireless interrogation using a conventional RFID reader. The unique advantage of this approach is that the sensor does not require batteries and does not need additional maintenance during its lifetime. This is an important feature in a high voltage environment where any maintenance requires either an outage or special equipment. The UHF RFID-based magnetic field was fabricated and tested in a laboratory experimental setup, which consists of a 34 mm diameter aluminum conductor typically used in a 500 kV HVdc system carrying a dc current of up to 1200 A. The sensor was attached to the conductor such that the Hall effect device was 30 mm from its surface. A dc current in the range of 100–1200 A was measured with an accuracy of 5% for a reader-to-sensor distance of 3 m.
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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) | 0.001 | 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 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".