Design and Testing of a Frequency-Selective Grounding for $3\phi$ Power Transformers
Bibliographic record
Abstract
Three-phase (3φ) power transformers can have their windings configured to create local neutral points, which can be connected to ground. The grounding of a 3φ power transformer is intended to limit ground currents (including fault currents) and limit ground potentials. These objectives can be translated into the resistive grounding that is usually designed based on system ratings and transformer parameters. The resistive grounding, however, can impact the flow of the harmonic components present in the exciting currents of a 3φ power transformer. As a result, undesired harmonic components are induced in primary and secondary voltages. This article presents the design and performance of a frequency-selective grounding that can achieve the objectives of grounding a 3φ power transformer. The developed grounding is designed to provide a resistive path for low-frequency currents (faults), and to create a low impedance path for high-frequency currents (harmonics). The frequency-selective grounding is experimentally tested for a 3φ power transformer with different primary and secondary winding configurations, different fault types, and source grounding. Test results show that the developed grounding can reduce the ground potential, harmonic distortion in primary and secondary voltages, and ground fault currents with a minimum interference with ground fault protective devices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".