Second Harmonic-Based Approach to Identify a GIC Flow in Power Transformers
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$3\phi$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$2{\text{nd}}$harmonic-based method is found able to accurately distinguish GIC flows from magnetizing inrush and fault currents.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".