TMR sensors as backup to conventional CTs for power systems protection applications
Bibliographic record
Abstract
This study investigates the use of TMR sensors as a backup to conventional current transformers (CTs) in power system protection. A primary focus is the calibration of TMR sensors by extracting fundamental frequency components from current waveforms to ensure accurate performance. The calibration process employs the primary CT as a reference under normal operating conditions. An algorithm extracts the CT's fundamental frequency component, providing a benchmark for calibrating the TMR sensor. The calibration adjusts the TMR sensor's amplitude and phase characteristics to align with the CT's output. This method is tested across diverse load conditions and fault scenarios to validate accuracy and consistency. Once calibrated, the TMR sensor functions as a redundant backup within the protection scheme. It operates alongside the CT during normal conditions but can seamlessly take over if the CT saturates or fails. This redundancy improves system reliability, ensuring continuous current measurement during extreme faults. Experimental results confirm that the calibrated TMR sensors provide accurate and reliable measurements comparable to CTs during normal conditions. They maintain signal integrity under high fault currents, avoiding the saturation and distortion issues common in CTs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".