Second Harmonic-Based Approach to Identify a GIC Flow in Power Transformers
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Bibliographic record
Abstract
Certain parts of the world can experience active geomagnetic disturbances, which trigger the flow of geomagnetically induced currents (GICs) through grounding circuits into power systems. Such currents are quasi-dc currents that can have large magnitudes capable of inflecting damage to power system equipment. Power transformers are among the vulnerable equipment to GIC flows, which can cause harmonic distortion, disruption in reactive power flow, and thermal damage in transformer windings. Adverse effects of GIC flows can be minimized by developing a fast, accurate, and reliable detection method of GICs. Such a detection method can support initiating adequate responses to block the GIC from flowing into power transformers. This paper presents the development and implementation of a method to detect GIC flows in power transformers. The developed method is based on extracting the second harmonic present in the differential currents. The proposed GIC detection is experimentally tested using laboratory <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3\phi$</tex-math></inline-formula> transformers, when operated for different GIC flows and loading levels. In addition, experimental tests are conducted for magnetizing inrush currents and various faults in both sides of tested transformers. Test results demonstrate fast and accurate detection of GIC flows with minor sensitivity to the value of the GIC, loading level, and/or transformer core type. In addition, the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2{\text{nd}}$</tex-math></inline-formula> harmonic-based method is found able to accurately distinguish GIC flows from magnetizing inrush and fault currents.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it