Effects of Loading Levels on Harmonic Distortion in Power Transformers Due to GIC Flows
Bibliographic record
Abstract
Geomagnetically induced currents (GICs) are currents dominated by dc components that flow through grounding circuits into power systems. These currents are typically initiated by geomagnetic disturbances, and flow into power systems through power transformers with grounded windings. The flow of a GIC through a power transformer creates adverse impacts, including high levels of harmonic distortion in the currents flowing through the grounded windings, overheating of transformer windings, and significant disruptions in the reactive power flow through the affected transformer. Adverse impacts of GICs on power transformers depend on various factors, among which are the core design (multi-cores, 3-limb, or 5-limb), configuration of primary and secondary windings, and loading levels. This paper presents and discusses the effects of loading levels on the harmonic distortion due to GIC flows in power transformers. Tests are carried out using a laboratory power transformer with multi-core (three single phase transformers). Various values of GIC flows are tested for different loading levels. Test results conclude that the loading levels have minor ifluence on the harmonic distortion created by the GIC flow. In addition, test results show that the 2ndharmonic component remains the dominant harmonic component due to the GIC flow in a power transformer, regardless of the loading level.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".