A Method to Detect DC Bias in Transformers Using Differential Current Waveforms
Bibliographic record
Abstract
This paper proposes a DC bias detection method for the power transformers, which utilizes the three-phase differential current waveforms available in the differential relays. It is shown that the DC bias condition can be detected from the asymmetry of the differential current waveforms, which occurs due to the transformer core saturation under the DC bias. It is also shown that the proposed scheme can identify the DC bias with and without the current transformer (CT) saturation. Furthermore, time-domain simulations verify the method's effectiveness in discriminating between the DC bias and transformer energization conditions. The proposed method is easy to implement and contributes to the power system monitoring by providing reliable DC bias detection for the transformers in the conditions such as hybrid AC-DC systems, monopolar operation of HVDC systems, and the geomagnetically induced current (GIC) flow due to a solar geomagnetic storm.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".