A Waveform-Based Approach for Transformer Differential Protection Under GIC Conditions
Bibliographic record
Abstract
Harmonic blocking (HB)-based differential relays, generally employed in the transformer protection, are likely to fail to interrupt internal short-circuit faults that occur under geomagnetically induced current (GIC) conditions. This paper proposes a waveform-based approach that enhances the differential protection dependability under the GIC conditions by detecting the GIC and overriding the HB signals for the internal faults. A novel algorithm is developed for the GIC detection, which employs three-phase instantaneous differential current waveforms and recognizes a GIC condition if the waveforms are all asymmetric in one direction. The proposed waveform-based approach also utilizes trip request signals of the differential relay's fault detection module to unblock the relay for the internal faults during the GIC. The results show that the proposed waveform-based approach (i) can successfully unblock the differential protection for the internal faults under the GIC conditions, (ii) does not malfunction for the external faults during the GIC, (iii) discriminates transformer energization cases from the GIC conditions, and (iv) is immune to the measurement noise. The current transformer (CT) saturation is also considered in the studies. The proposed approach's effectiveness is verified for different transformer types, including three-phase and single-phase transformers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".