Transformer Differential Protection During Geomagnetic Disturbances: A Hybrid Protection Scheme and Real-Time Validation Tests
Bibliographic record
Abstract
The transformer differential relays utilize harmonic blocking modules that are prone to block the relay operation for internal faults under the geomagnetically induced current (GIC) flow during geomagnetic disturbances (GMDs). In this paper, by running hardware-in-the-loop (HIL) tests on an advanced commercial differential relay, it is proven that the relay fails to operate for the in-zone terminal and turn faults due to the undesired harmonic blocking under the GIC condition, with and without current transformer (CT) saturation. A new protection scheme is proposed to ensure the relay unblocking during the GIC. The proposed scheme utilizes a hybrid GIC detection method that identifies the GIC conditions based on the raw/unfiltered waveforms and second harmonic phasors of differential currents. The performance of the waveform-based part of the hybrid method is improved by an average-based algorithm detecting the GIC by monitoring the average, maximum, and minimum values of differential current waveforms. The proposed scheme's dependability and security are extensively investigated in GIC- and non-GIC-based scenarios, including internal and external faults and inrush currents. Furthermore, the real-time tests prove the effectiveness of the proposed scheme in unblocking the relay for the in-zone faults while the transformer is subjected to the GIC.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".