Thermal Analysis of Power Transformers Under Geomagnetically Induced Current
Bibliographic record
Abstract
Temperature distribution in the power transformers is investigated in this study under Geomagnetically Induced Current (GIC) conditions. Thermal stress can significantly reduce the insulation life, and in the case of excessive hot spot temperature (HST), catastrophic failure of transformers is likely, as happened in the past Geomagnetic Disturbance (GMD) events. Although a few reports emphasize the impact of GIC on the local heating within the transformers, especially in the structural parts, the effect of GIC on the thermal condition of transformers has not been investigated profoundly. This article studies the power transformer HST during the GIC. Since finding stray losses under GIC conditions is challenging, a hybrid approach, including a topological transformer model and 3D finite element method (FEM), is implemented. The detailed topological transformer model is utilized in the EMTP time-domain simulations to determine the harmonic currents at different GIC levels. Additionally, FEM is employed to calculate the temperature distribution within the transformer. The simulation results reveal that the structural parts are saturated with low GIC magnitudes, resulting in high stray losses and local hot spot heating in those areas. Furthermore, the tank can reach high temperatures at mid-GIC levels. These results clearly show that the transformer structural parts are highly vulnerable under severe GIC situations.
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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 | 0.000 | 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 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".